JP2006212054A - Ultrasonic observation apparatus, and image processing apparatus and program - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide an ultrasonic observation apparatus and the like capable of analyzing speckle information included in an ultrasonic image whole body and displaying the analysis result into a moving image. <P>SOLUTION: This ultrasonic observation apparatus body is provided with a receiving/processing means 26 creating original data by processing receiving signals obtained based on an ultrasonic echo generated by reflecting ultrasonic beams transmitted from an ultrasonic transducer, from a subject, a speckle analysis means 29 creating speckle image data based on the original data created by the receiving/processing means 26 and analyzing the speckle image expressed by the created speckle image data, and a display means 34 displaying the analysis result in the speckle analysis means 29. <P>COPYRIGHT: (C)2006,JPO&NCIPI

Description

  The present invention relates to an ultrasonic observation apparatus, an image processing apparatus, and an image used for generating an ultrasonic image based on an ultrasonic image signal obtained by transmitting ultrasonic waves and receiving ultrasonic echoes. It relates to a processing program.

In the medical field, various imaging techniques have been developed in order to observe and diagnose the inside of a subject. In particular, ultrasonic imaging that acquires internal information of a subject by transmitting and receiving ultrasonic waves enables real-time image observation, and other medical uses such as X-ray photographs and RI (radio isotope) scintillation cameras. Unlike imaging technology, there is no radiation exposure. Therefore, ultrasonic imaging is used as a highly safe imaging technique in a wide range of areas including gynecological system, circulatory system, digestive system, etc. in addition to fetal diagnosis in the obstetrics field.

  Ultrasonic imaging is an imaging technique that generates an image based on the following principle. Ultrasonic waves are reflected at boundaries between regions having different acoustic impedances, such as boundaries between structures. Therefore, by transmitting an ultrasonic beam into a subject such as a human body, receiving an ultrasonic echo generated in the subject, and determining the reflection point and reflection intensity at which the ultrasonic echo was generated, It is possible to extract the contour of the structure (eg, internal organs or lesion tissue).

  By the way, in an ultrasonic image obtained by imaging a structurally non-uniform subject such as a living body, a pattern in which bright portions and / or dark portions are scattered appears. Such a pattern is called a speckle pattern, and is generated, for example, by interference of ultrasonic echoes reflected by a non-uniform tissue existing inside an internal organ or the like. Since this speckle pattern is a kind of virtual image, the contour of the drawn structure is often unclear.

  In Patent Document 1, in an ultrasonic diagnostic apparatus that obtains a tomographic image by irradiating a subject with an ultrasonic pulse, a specific signal is used using the statistical properties of the intensity or amplitude information of an echo signal generated from the subject site. There is disclosed an ultrasonic diagnostic apparatus that includes an analysis calculation means for extracting a signal and a display means for displaying a result extracted by the analysis calculation means. According to this ultrasonic diagnostic apparatus, images are smoothed using the statistical properties of the speckle pattern, and by extracting a fine structure, a homogeneous tissue structure such as the degree of progression of liver cirrhosis is obtained. It is possible to observe minute abnormal lesions inside.

  In Patent Document 2, a region of interest is set on an ultrasound image, and binarization processing is performed while scanning a threshold value for an image in the region of interest. An ultrasonic diagnostic apparatus is disclosed in which the number of high-luminance islands is calculated and a speckle evaluation graph is created as a histogram thereof. According to this ultrasonic diagnostic apparatus, the speckle evaluation graph to be created reflects the tissue characteristics, and thus tissue diagnosis can be performed by numerically analyzing the form of this graph. In Patent Document 2, prior to the binarization process, a process for emphasizing speckles (a process for excluding the base component) is performed, and the image before the binarization process and the image after the binarization process are processed. Displaying images simultaneously is also disclosed.

Japanese Patent Laying-Open No. 2003-61964 (first page, FIG. 1) JP 2004-41617 A (first page)

  As described above, speckles are considered to contain information related to tissue properties. However, the above Patent Documents 1 and 2 analyze the inside of a set region of interest, and speckle stillness. An image is displayed. However, in order to make more effective use of information related to tissue properties contained in speckles, it is possible to analyze speckle information contained in the entire ultrasound image and display the analysis result as a moving image. Is desired by users.

  Therefore, in view of the above points, the present invention is an ultrasonic observation apparatus, an image processing apparatus, and an image processing that can analyze speckle information included in the entire ultrasonic image and display the analysis result as a moving image. The purpose is to provide a program.

  In order to solve the above-described problem, an ultrasonic observation apparatus according to one aspect of the present invention is a reception signal obtained based on an ultrasonic echo generated by reflecting an ultrasonic beam transmitted from an ultrasonic transducer from a subject. A reception processing means for generating original data by processing the data, and generating speckle image data based on the original data generated by the reception processing means, and the speckle image represented by the generated speckle image data. Speckle analysis means for performing analysis, and display means for displaying an analysis result in the speckle analysis means.

  An image processing apparatus according to one aspect of the present invention is generated by processing a reception signal obtained based on an ultrasonic echo generated by reflection of an ultrasonic beam transmitted from an ultrasonic transducer from a subject. Speckle image generation means for generating speckle image data based on the original data, speckle image analysis means for analyzing the speckle image represented by the speckle image data generated by the speckle image generation means, It comprises.

  Furthermore, an image processing program according to one aspect of the present invention processes a reception signal obtained based on an ultrasonic echo generated by reflection of an ultrasonic beam transmitted from an ultrasonic transducer from a subject. Speckle image generation means for generating speckle image data based on the original data generated by, and a speckle image for analyzing the speckle image represented by the speckle image data generated by the speckle image generation means It is for functioning as an analysis means.

  According to the present invention, the speckle analysis result image obtained by imaging the inter-frame difference of the speckle image, the change in the depth of the low frequency component intensity or the high frequency component intensity included in the speckle image for one frame is imaged. A speckle analysis result image, a speckle analysis result image obtained by imaging a change due to a depth of a difference between a low frequency component intensity included in the first speckle image and a high frequency component intensity included in the second speckle image, A speckle analysis result image obtained by imaging a change between frames of a change due to the depth of the low frequency component intensity included in the first speckle image or a change due to the depth of the high frequency component intensity included in the first speckle image; And the change of the difference between the low frequency component intensity included in the first speckle image and the high frequency component intensity included in the second speckle image due to the depth. Speckle analysis result images that visualize changes between frames can be generated and displayed as moving images, facilitating medical diagnosis by doctors and improving diagnostic efficiency, and improving the quality of medical diagnosis It becomes possible to improve.

Hereinafter, the best mode for carrying out the present invention will be described in detail with reference to the drawings. The same constituent elements are denoted by the same reference numerals, and the description thereof is omitted.
FIG. 1 is a block diagram showing an ultrasonic observation apparatus according to an embodiment of the present invention. This ultrasonic observation apparatus controls the ultrasonic probe 10 that transmits and receives ultrasonic waves, and the transmission and reception of ultrasonic waves, and also acquires ultrasonic images based on reception signals obtained by detecting ultrasonic echoes. And an ultrasonic observation apparatus main body to be generated.

The ultrasonic probe 10 includes an ultrasonic transducer array in which a plurality of ultrasonic transducers are arranged. Each ultrasonic transducer is, for example, a piezoelectric ceramic represented by PZT (Pb (lead) zirconate titanate) or PVDF.
It is produced by forming electrodes on both ends of a piezoelectric material (piezoelectric material) such as a polymer piezoelectric element represented by (polyvinylidene difluoride). When a voltage is applied by sending a pulsed electric signal or a continuous wave electric signal to the electrodes of such an ultrasonic transducer, the piezoelectric body expands and contracts to generate ultrasonic waves. Therefore, a plurality of ultrasonic transducers are electronically controlled to generate pulsed or continuous ultrasonic waves from the respective ultrasonic transducers. Thereby, an ultrasonic beam is formed by synthesizing those ultrasonic waves, and the subject is electronically scanned. The plurality of ultrasonic transducers expand and contract by receiving propagating ultrasonic waves and generate electrical signals. These electric signals are output as ultrasonic reception signals. Such an ultrasonic probe 10 is connected to the ultrasonic observation apparatus main body via a cable.

  As the ultrasonic probe 10, a linear array probe in which a plurality of ultrasonic transducers are arranged one-dimensionally, a sector probe that can scan the inside of a subject in a fan shape, or a plurality of ultrasonic transducers arranged on a convex surface A convex array probe or the like is used. A two-dimensional array probe in which a plurality of ultrasonic transducers are two-dimensionally arranged may be used. In this case, ultrasonic images relating to a plurality of different cross sections can be obtained without mechanically moving the ultrasonic probe. Furthermore, in an ultrasonic endoscope, a body cavity probe that performs radial scanning may be used. Radial scanning is a scanning method in which the ultrasonic transmission direction is rotated and the received ultrasonic signal is imaged in synchronization with the rotation, and 360 ° around the probe can be displayed at a time. . In that case, a mechanical radial probe that transmits ultrasonic waves while rotating the probe may be used, or an electronic radial that rotates the transmission direction of ultrasonic waves by electronic control without rotating the probe itself. A probe may be used.

The ultrasonic observation apparatus main body includes a control unit 20, a scanning control unit 21, a drive signal generation unit 22, a transmission / reception switching unit 23, a signal processing unit 24, an A / D (analog / digital) converter 25, Phase matching unit 26, primary memory 27, speckle analysis unit 29, image selection unit 30, DSC (digital scan converter) 31, secondary memory 32, and D / A (digital / analog) converter 33, a display unit 34, an operation console 35, and a recording unit 36 are included.
Here, the control unit 20 is configured by a computer (CPU) and software, and controls each unit of the ultrasonic observation apparatus main body. The speckle analysis unit 29, the image selection unit 30, and the DSC 31 constitute an image processing apparatus according to the present invention.

  The scanning control unit 21 is given to a drive signal for driving a plurality of ultrasonic transducers included in the ultrasonic probe 10 according to the direction in which the ultrasonic waves are transmitted under the control of the control unit 20. Set the delay time. Further, when a mechanical radial probe is used as the ultrasonic probe 10, the scanning control unit 21 controls the operation of a motor that rotates the probe and transmits the ultrasonic wave in synchronization with the operation. To control.

  The drive signal generation unit 22 includes a plurality of pulsers respectively corresponding to the plurality of ultrasonic transducers included in the ultrasonic probe 10. Each pulser generates a drive signal at a set timing under the control of the scanning control unit 21. Thereby, ultrasonic waves are respectively generated from the plural ultrasonic transducers with a predetermined time difference.

  The transmission / reception switching unit 23 inputs the drive signal generated by the drive signal generation unit 22 to the ultrasonic probe 10 and captures a reception signal in the signal processing unit 24 described later in accordance with the control of the control unit 20. Switch at the timing. In this way, by limiting the reading signal reading time zone, an ultrasonic echo signal reflected from a specific depth of the subject is detected.

The signal processing unit 24 includes a plurality of channels respectively corresponding to the plurality of ultrasonic transducers. Each of these channels captures a reception signal output from the corresponding ultrasonic transducer at a predetermined timing, and performs signal processing such as amplification and Nyquist filter processing.
The A / D converter 25 digitally converts the analog signal processed by the signal processing unit 24 to generate reception data.

The phase matching unit 26 performs reception focus processing by delaying and adding a plurality of digitally converted reception data. Thereby, reception data (sound ray data) representing a reception beam whose focus is narrowed down in a predetermined sound ray direction is generated.
Furthermore, by detecting the waveform represented by the sound ray data, image data representing luminance values in a plurality of pixels constituting the ultrasonic image can be obtained. Hereinafter, this image data is referred to as original data.
The primary memory 27 sequentially stores the original data obtained in the phase matching unit 26. This original data includes information on structures in the subject and information on speckle patterns.

  The speckle analysis unit 29 operates under the control of the control unit 20 and generates speckle image data for each frame based on the original data read from the primary memory 27. A speckle image analysis unit 42 that analyzes speckle images represented by speckle image data generated by the speckle image generation unit 41 and generates speckle analysis result image data indicating the analysis result. .

  FIG. 2 is a block diagram illustrating a configuration of the speckle image analysis unit 42 of the speckle analysis unit 29. As shown in FIG. 2, the speckle image analysis unit 42 switches the speckle image data input from the speckle image generation unit 41 for each frame under the control of the control unit 20. Switches 51 that alternately output from the output terminal, a first speckle image memory 52 for storing speckle image data for one frame input from the first output terminal of the switch 51, A second speckle image memory 53 for storing speckle image data for one frame input from two output terminals, and a speckle image for one frame input from the first speckle image memory 52. By calculating the difference between the value of the data and the value of the speckle image data for one frame input from the second speckle image memory 53, the speckle And a difference calculation unit 54 for generating an inter-frame change speckle analysis result image data was imaged (the change between frames of the maximum point and minimum point in the original data).

  Under the control of the control unit 20, the image selection unit 30 receives original data input from the primary memory 27, speckle image data input from the speckle image generation unit 41, and speckle image analysis unit 42. Image data to be output to the DSC 31 is selected from the input speckle analysis result image data.

The DSC 31 converts the image data in the ultrasonic beam scanning space into B-mode image data for display in the physical space for the image data selected by the image switching unit 30 by converting the scanning format. That is, the DSC 31 performs resampling corresponding to the image display range and coordinate conversion and interpolation corresponding to the ultrasonic scanning method. For example, image data obtained by linear scanning is subjected to an interpolation process for generating a linear image. Also, polar coordinate conversion and interpolation processing are performed on image data obtained by sector scanning, convex scanning, or radial scanning.

In addition, the DSC 31 selects the original data and the speckle analysis result image data selected by the image selection unit 30 and input them under the control of the control unit 20. A process of generating composite image data obtained by combining the speckle analysis result image and generating parallel image data in which the original image and the speckle analysis result image are arranged in parallel is also performed.
Here, when generating parallel image data, the DSC 31 sets the size of the original image and the speckle analysis result image to the first mode parallel image data in which the original image and the speckle analysis result image are arranged in the same size. Select one of the second mode parallel image data arranged larger than the size and the third mode parallel image data arranged with the original image size smaller than the speckle analysis result image size. And generate.

  An STC (sensitivity time control) for correcting distance attenuation is provided before the DSC 31, or linear gradation processing including gain adjustment and contrast adjustment and γ correction are included after the DSC 31. An image processing unit that performs image processing such as nonlinear gradation processing may be provided.

The secondary memory 32 stores the B-mode image data for display input from the DSC 31 in a format capable of raster scanning, for example. The D / A converter 33 converts the image data read from the secondary memory 32 into an analog signal and outputs the analog signal.
The display unit 34 is, for example, a raster scan CRT display or LCD display, and displays an ultrasonic image based on an analog-converted image signal.

  The console 35 is used when inputting various commands and information to the ultrasonic observation apparatus main body. The console 35 includes a speckle analysis mode button 44 for instructing the start of speckle analysis in the speckle analysis unit 29, an image selection unit control button 45 for instructing image selection in the image selection unit 30, and a DSC 31. DSC control button 46 for instructing selection of composite image / parallel image and selection of parallel image mode, input device such as keyboard and touch panel, pointing device such as mouse, adjustment knob, input button, etc. It is out.

  The recording unit 36 includes a basic program for causing the CPU (control unit 20) included in the ultrasonic observation apparatus main body to execute an operation, a control program (software) for performing various processes, and information used for the processes. And the like. In addition to the built-in hard disk, an external hard disk, flexible disk, MO, MT, RAM, CD-ROM, or DVD-ROM may be used as the recording medium.

Next, the operation of the ultrasonic observation apparatus shown in FIG. 1 will be described with reference to FIGS.
For example, when displaying the first mode parallel image in which the original image and the speckle analysis result image are arranged in the same size on the display unit 34, the user presses the speckle analysis mode button 44 of the console 35, The image selection unit control button 45 is used to instruct the image selection unit 30 to select the original image and the speckle analysis result image, and the DSC control button 46 is used to instruct the DSC 31 to generate the first mode parallel image.

  Thereafter, when the user starts ultrasonic imaging, the ultrasonic probe 10 shown in FIG. 1 transmits an ultrasonic beam under the control of the control unit 20, and performs linear scanning, sector scanning, or convex scanning. Alternatively, the subject is scanned by a scanning method such as radial scanning. The ultrasonic beam is reflected by a reflector present in the subject, and a plurality of ultrasonic echoes are received by the ultrasonic probe 10. The received ultrasonic echo is converted into an electric signal by the ultrasonic probe 10 and input to the main body of the ultrasonic observation apparatus as a received signal.

  The plurality of received signals input to the ultrasonic observation apparatus main body are subjected to predetermined signal processing in the signal processing unit 24, subjected to phase matching processing and detection processing after A / D conversion, and are temporarily converted into primary data. Stored in the memory 27. When original data (frame data) for one frame is accumulated in the primary memory 27, the original data is output to the speckle analysis unit 29.

  FIG. 3 shows an ultrasonic image (original image) represented by the accumulated original data. Moreover, the curve (1) shown in FIG. 4 represents the original data (luminance value) regarding the area | region shown with the dashed-dotted line AA 'of FIG. 3, and the curve (2) is the structure in dashed-dotted line AA'. It represents a signal related to an object. As shown in FIG. 4, the original data that has been subjected to phase matching includes a signal representing a structure and a signal representing a speckle pattern overlapping therewith. Therefore, as shown in FIG. 3, the original image contains a mixture of structures and speckle patterns.

  Next, the speckle image generation unit 41 of the spec analysis unit 29 extracts structure image data from the original data shown in FIG. For this purpose, first, the speckle image generation unit 41 obtains a signal representing the maximum point and a signal representing the minimum point in the original data. As shown in FIG. 4, the signal representing the maximum point is obtained by obtaining a differential value at each point represented by the original data shown in the curve (3), and the differential value changes from positive to negative among those differential values. It is obtained by finding points and then linearly interpolating between those points. Similarly, the signal representing the minimum point is obtained from the differential value at each point represented by the original data shown in the curve (3) by obtaining a point where the differential value changes from minus to plus, and linearly between these points. Obtained by interpolation. In FIG. 4, a curve (4) shows a signal representing the maximum point, and a curve (5) shows a signal representing the minimum point.

  Here, when the maximum or minimum point in the original data is extracted, if a simple determination is made based on the differential value in the original data, the maximum / minimum point generated by the speckle and the maximum / minimum point generated by the structure are obtained. There is a case where the local minimum point is confused. Therefore, when determining whether or not to adopt a certain maximum point or minimum point, the distance to the previously extracted maximum point or minimum point is calculated, and the distance is longer than the wavelength of the transmitted ultrasonic wave It is desirable to add a condition that the point is not adopted.

Next, the speckle image generation unit 41 obtains a signal representing the average value of the signal representing the maximum point and the signal representing the minimum point. A curve (6) in FIG. 5 shows a signal representing an average value. A signal representing this average value becomes structure image data representing an ultrasonic image (structure image) of the structure in the imaging region. Further, the speckle image generation unit 41 calculates speckle image data by subtracting the value represented by the structure image data from the value represented by the original data. In that case, an offset value may be added to the difference value as necessary. As the offset value, a fixed value set in advance in the ultrasonic observation apparatus may be used, or a value input by the user may be used.
By performing such arithmetic processing for one frame of frame data, the speckle image shown in FIG. 6A and the structure image shown in FIG. 6B are obtained separately. Can do.

  Here, in the present embodiment, when a signal representing a structure is obtained, the local maximum point and the local minimum point in the original data are used instead of general filter processing for the following reason. In other words, the size of the speckle pattern (speckle size) displayed in the imaging area differs depending on the depth of the imaging area. For this reason, if filtering is performed uniformly on the original data, speckles may not be completely removed, or conversely, signals representing structures may be removed.

Based on the value (speckle image data value) represented by the generated speckle image data and the value (structure image data value) represented by the structure image data, the speckle image generation unit 41 The value of the mixed data (mixed data value) is calculated using an equation.
Mixed data value = structure image data value × K + speckle image data value × (1−K)
Here, K represents a mixing ratio between the structure image and the speckle image, and a user inputs a desired value in the range of 0 ≦ K ≦ 1 using the console 35. Thereby, as shown in FIG. 7, it is possible to obtain a mixed image in which the structure and the speckle pattern are mixed at a desired ratio. When K = 0, the mixed image is an image having only a speckle pattern (that is, a speckle image). When K = 1, the mixed image is an image having only a structure (that is, a structure image). )

  The speckle image data representing the speckle image generated by the speckle image generation unit 41 is switched by the switch 51 for each frame in the speckle image analysis unit 42, and the first and second speckle image memories 52. And 53 are alternately stored. Each time new speckle image data is stored in the first and second speckle image memories 52 and 53, two speckle images output from the first and second speckle image memories 52 and 53, respectively. Speckle as speckle analysis result image data indicating change information between frames of the local maximum point and local minimum point in the original data (speckle change information between frames) is calculated by the difference calculation unit 54. Difference image data between frames of the image is generated.

  The image selection unit 30 receives the original data input from the primary memory 27 and the speckle image analysis unit 42 in accordance with a control signal from the control unit 20 that instructs selection of the original image and the speckle analysis result image. Speckle analysis result image data (speckle image inter-frame difference image data) is selected and output to the DSC 31.

  The DSC 31 performs the original image and the speckle analysis result based on the original data and the speckle analysis result image data input from the image selection unit 30 according to the control signal from the control unit 20 instructing the generation of the first mode parallel image. A first mode parallel image in which images are arranged in the same size is generated.

  Image data representing the first mode parallel image generated by the DSC 31 is converted to an analog signal by the D / A converter 33 and output to the display unit 34. Accordingly, the first mode parallel image in which the original image and the speckle analysis result image (difference image between frames of the speckle image) are arranged in the same size is displayed on the display unit 34. At this time, since the original image and the speckle analysis result image can be displayed continuously for each frame, the first mode parallel image in which the original image and the speckle analysis result image are arranged in the same size is displayed as a moving image. Can be displayed on the unit 34.

As described above, according to the present embodiment, an inter-frame difference image of a speckle image can be generated and displayed on the display unit 34 as a moving image. In addition, a composite image obtained by combining the original image and the speckle analysis result image, or a parallel image in which the original image and the speckle analysis result image are arranged can be displayed on the display unit 34 according to the user's preference. . Therefore, by displaying an appropriate moving image on the screen according to the purpose of diagnosis, it is possible to facilitate diagnosis by a doctor and improve the quality of diagnosis.

Next, another configuration of the speckle analysis unit 29 shown in FIG. 2 will be described with reference to FIGS.
In the speckle analysis unit 29 illustrated in FIG. 8, the speckle image generation unit 41 includes an LPF (low-pass filter) 61 that extracts a low-frequency component of an image signal represented by original data input from the primary memory 27. The first speckle image generation unit 62 that generates the first speckle image data based on the low frequency component extracted by the LPF 61 and the image signal represented by the original data input from the primary memory 27 An HPF (high-pass filter) 63 that extracts a high-frequency component and a second speckle image generation unit 64 that generates second speckle image data based on the high-frequency component extracted by the HPF 63 are included.

  Here, the LPF 61 and the HPF 63 have frequency characteristics as shown in FIG. That is, the LPF 61 extracts only the low frequency band from the original signal band, while the HPF 63 extracts the high frequency from the original signal band, with the center frequency of the original signal band (the frequency band of the image signal represented by the original data) being a cutoff frequency. Extract only the band.

  Further, the speckle image analysis unit 42 selects one of the speckle image data input from the first and second speckle image generation units 62 and 64 under the control of the control unit 20 and outputs it from the output terminal. By applying the following frequency processing to the output switch 65 and the speckle image data for one frame input from the switch 65, the low frequency component intensity or the high frequency included in the speckle image for one frame is applied. And a frequency processing unit 66 that images changes due to the depth of the frequency component intensity.

As illustrated in FIG. 10, the frequency processing unit 66 extracts the speckle signal from the speckle image data for one frame for each line, and then performs a center t and a center on the extracted speckle signal for one line. Waveform / frequency conversion processing (FFT processing, wavelet processing, etc.) is performed for the window width W section. Thereafter, the frequency processing unit 66 calculates an integrated value of the frequency component obtained by the waveform / frequency conversion process, and sets the calculated integrated value as a representative value of the center t (time or distance). The frequency processing unit 66 performs such waveform / frequency conversion processing in a range of W / 2 <t <T−W / 2 (T is a time or distance for one line), and represents a representative value of each center t. Is calculated. Further, the frequency processing unit 66 performs such waveform / frequency conversion processing by changing the window width W within a possible range in the range of 0 <t <W / 2 and TW / 2 <t <T. The representative value is calculated. In this way, by calculating representative values for 0 <t <T for all lines, low frequency component intensity or low frequency component intensity included in a speckle image for one frame is visualized as a low frequency. Component intensity image data or high frequency component intensity image data is obtained as speckle analysis result image data.

Next, still another configuration of the speckle analysis unit 29 shown in FIG. 2 will be described with reference to FIG.
In the speckle analysis unit 29 shown in FIG. 11, the speckle image analysis unit 42 visualizes the change due to the depth of the difference between the low frequency component intensity and the high frequency component intensity included in the speckle image for one frame. Thus, it differs from the speckle analysis unit 29 shown in FIG. That is, the speckle image analysis unit 42 includes a first frequency processing unit 75, a second frequency processing unit 76, and a difference calculation unit 77.

The first frequency processing unit 75 performs the frequency processing shown in FIG. 10 on the first speckle image data input from the first speckle image generation unit 75 to thereby generate the first speckle image. Low-frequency component intensity image data representing a change in the depth of the low-frequency component intensity included in is generated.
The second frequency processing unit 76 performs the frequency processing shown in FIG. 10 on the second speckle image data input from the second speckle image generation unit 74 to thereby generate the second speckle image. High-frequency component intensity image data representing changes in the depth of the high-frequency component intensity included in is generated.
The difference calculation unit 77 includes a value of the low frequency component intensity image data input from the first speckle image generation unit 75 and a value of the high frequency component intensity image data input from the second speckle image generation unit 76. Speckle analysis result of imaging change due to depth of difference between low frequency component intensity included in first speckle image and high frequency component intensity included in second speckle image by calculating difference between Generate image data.

Next, still another configuration of the speckle analysis unit 29 shown in FIG. 2 will be described with reference to FIG.
The speckle analysis unit 29 shown in FIG. 12 is such that the speckle image analysis unit 42 visualizes the low-frequency component strength or the high-frequency component strength change included in the speckle image between frames. 11 is different from the speckle analysis unit 29 shown in FIG. That is, the speckle image analysis unit 42 includes a first switch 85, a frequency processing unit 86, a second switch 87, a first frequency processing memory 88, a second frequency processing memory 89, and a difference calculation. Part 90.

The first switch 85 selects one of the speckle image data input from the first and second speckle image generation units 82 and 84 under the control of the control unit 20 and outputs the selected one from the output terminal.
The frequency processing unit 86 performs the frequency processing shown in FIG. 10 on the first or second speckle image data input from the first switch 85, thereby reducing the low frequency included in the first speckle image. Low frequency component intensity image data representing a change due to depth of frequency component intensity, or high frequency component intensity image data representing a change due to depth of high frequency component intensity included in the second speckle image is generated.

The second switch 87 switches the low-frequency component intensity image data or the high-frequency component intensity image data input from the frequency processing unit 86 for each frame under the control of the control unit 20, and outputs the first and second outputs. Outputs alternately from the terminal.
The first frequency processing memory 88 stores low frequency component intensity image data or high frequency component intensity image data input from the first output terminal of the second switch 87. The second frequency processing memory 89 stores low frequency component intensity image data or high frequency component intensity image data input from the second output terminal of the second switch 87.

  The difference calculation unit 90 includes the value of the low frequency component intensity image data or the high frequency component intensity image data read from the first frequency processing memory 88 and the low frequency component intensity image data read from the second frequency processing memory 88 or By calculating a difference from the value of the high frequency component intensity image data, a change due to the depth of the low frequency component intensity included in the first speckle image or a depth of the high frequency component intensity included in the second speckle image Speckle analysis result image data in which changes between frames due to the change are imaged is generated. Thereby, the moving object can be captured.

Next, still another configuration of the speckle analysis unit 29 shown in FIG. 2 will be described with reference to FIG.
In the speckle analysis unit 29 illustrated in FIG. 13, the speckle image analysis unit 42 determines the difference between the low frequency component intensity included in the first speckle image and the high frequency component intensity included in the second speckle image. It differs from the speckle analysis unit 29 shown in FIGS. 8, 11, and 12 in that the change between frames due to the depth is imaged. That is, the speckle image analysis unit 42 includes a first frequency processing unit 95, a second frequency processing unit 96, a first difference calculation unit 97, a switch 98, a first frequency processing memory 99, A second frequency processing memory 100 and a second difference calculation unit 101 are included.

The first frequency processing unit 95 performs the frequency processing shown in FIG. 10 on the first speckle image data input from the first speckle image generation unit 92, whereby the first speckle image is displayed. Low-frequency component intensity image data representing a change in the depth of the low-frequency component intensity included in is generated.
The second frequency processing unit 96 performs the frequency processing shown in FIG. 10 on the second speckle image data input from the second speckle image generation unit 94 to thereby generate the second speckle image. High-frequency component intensity image data representing changes in the depth of the high-frequency component intensity included in is generated.

  The first difference calculation unit 97 includes the value of the low frequency component intensity image data input from the first speckle image generation unit 95 and the high frequency component intensity image data input from the second speckle image generation unit 96. By calculating the difference between the low frequency component intensity and the low frequency component intensity, the difference between the low frequency component intensity included in the first speckle image and the high frequency component intensity included in the second speckle image is represented by a change in depth. Frequency component intensity difference image data is generated.

The first switch 98 switches the low and high frequency component intensity difference image data input from the first difference calculation unit 97 for each frame under the control of the control unit 20 and outputs first and second output terminals. Output from.
The first frequency processing memory 99 stores low / high frequency component intensity difference image data input from the first output terminal of the first switch 98. The second frequency processing memory 100 stores low / high frequency component intensity difference image data input from the second output terminal of the first switch 98.

  The second difference calculation unit 101 reads the value of the low / high frequency component intensity difference image data read from the first frequency processing memory 99 and the low / high frequency component intensity difference image read from the second frequency processing memory 100. By calculating the difference with the data value, the difference between the low frequency component intensity included in the first speckle image and the high frequency component intensity included in the second speckle image is changed between frames. Speckle analysis result image data in which changes are imaged is generated.

In the above description, the switch 51 in FIG. 2, the second switch 87 in FIG. 12, and the switch 98 in FIG. 13 are operated under the control of the control unit 20, but under the control of the scanning control unit 21. It may be operated.
The speckle analysis unit 29 shown in FIGS. 8, 11, 12, and 13 performs frequency processing by dividing the original signal band into a low frequency band and a high frequency band. Frequency processing may be performed by dividing into the above bands (for example, three of a low frequency band, an intermediate frequency band, and a high frequency band).

  Further, the speckle image generation unit 41 in FIG. 1 may generate a speckle image using a two-dimensional mask process as described below. First and second speckle image generators 62 and 64 shown in FIG. 8, first and second speckle image generators 72 and 74 shown in FIG. 11, and first and second speckle image generators 72 and 74 shown in FIG. The same applies to the speckle image generation units 92 and 94.

FIG. 14 is a flowchart illustrating a speckle image generation method for generating a speckle image using two-dimensional mask processing. This speckle image generation method is performed on original data acquired by performing linear scanning, and at this time, two-dimensional mask processing is used.
14, the speckle image generation unit 41 illustrated in FIG. 1 extracts a local maximum point in the original image based on the original data stored in the primary memory 27. The maximum point can be extracted by the following method, for example. As shown in FIG. 15, paying attention to a certain pixel Y _n , the pixel value (luminance value) D (Y _n ) of the pixel Y _{n and} the pixel values D (Y _n1 ) of the pixels Y ₁ to Y ₈ located around the pixel Y _n. ) To D (Y _n8 ). When the pixel value D (Y _n ) is larger than the pixel values of surrounding pixels, that is, the following relational expressions D (Y _n )> D (Y _n1 ), D (Y _n )> D (Y _n2 ) _{, D (Y n)> D} (Y n3), D (Y n)> D (Y n4), D (Y n)> D (Y n5), D (Y n)> D (Y n6), D When all of (Y _n )> D (Y _n7 ) and D (Y _n )> D (Y _n8 ) are satisfied, Y _n is determined to be a local maximum point.

  Next, in step S12 of FIG. 14, the speckle image generation unit 41 calculates the pixel value of the interpolation point by four-point interpolation using a square interpolation mask or a flat interpolation mask in order to interpolate the extracted maximum point. To do.

FIG. 16 is a diagram for explaining a method of performing four-point interpolation using a square interpolation mask, and the hatched portion indicates the local maximum point extracted in step S11. As shown in FIG. 16A, in order to obtain four points used to calculate the pixel value of the interpolation point Y, first, the surrounding pixels are divided into four quadrants with the interpolation point Y as the center. . Then, the square interpolation masks M ₁ , M ₂ ,... Shown in FIG. 16B are sequentially used in ascending order of the mask size, so that the local maximum close to the interpolation point Y from the first quadrant to the fourth quadrant. Search for each point. Pixels Y _{1 to} Y ₄ shown in FIG. 16A indicate local maximum points searched in the first quadrant to the fourth quadrant, respectively.

Next, the pixel value D (Y) of the interpolation point Y is calculated based on the position of the searched pixels Y _{1 to} Y ₄ and the pixel values D (Y ₁ ) to D (Y ₄ ). Therefore, the pixel value D of the pixel _{Y 1 (Y 1),} by using the pixel value D of the pixel _{Y 2 (Y 2),} the pixel value D (Y at the point _{Y A} on one axis including the pixel Y and calculates the weighted average method _a), the pixel value D of the pixel _{Y 3 (Y 3),} the pixel value D _{(Y 4} pixels _{Y 4)} and using a point on one axis including the pixel Y and calculates the pixel value D _{(Y B)} at Y _B. Further, the pixel value D (Y) of the interpolation point Y is calculated by the weighted average method using the positions of the points Y _A and Y _B and the pixel values D (Y _A ) and D (Y _B ).

FIG. 17 is a diagram for explaining a method of performing four-point interpolation using a flat interpolation mask. In the present embodiment, a non-square mask that is long in the vertical direction of the drawing (that is, the depth direction in the subject) is used. As shown in FIG. 17A, in order to obtain four points used for calculating the pixel value of the interpolation point Y, first, the surrounding pixels are divided into four quadrants with the interpolation point Y as the center. . Then, by sequentially using the flat interpolation masks M ₁ ′, M ₂ ′,... Shown in FIG. 17B from the smaller mask size, the interpolation point Y is selected from the first quadrant to the fourth quadrant. Search for each local maximum point. In FIG. 17A, pixels Y _{5 to} Y ₈ indicate the maximum points in the first quadrant to the fourth quadrant, respectively.
Next, the weighted average method is used to calculate the pixel value D (Y) ′ of the interpolation point Y based on the searched positions of the pixels Y _{5 to} Y ₈ and the pixel values D (Y ₅ ) to D (Y ₈ ). Calculated by The calculation method of the pixel value D (Y) ′ is the same as that in the square interpolation mask.

Next, in step S13 of FIG. 14, interpolation data is calculated based on the pixel value D (Y) calculated using the square interpolation mask and the pixel value D (Y) ′ calculated using the flat interpolation mask. Is generated.
Here, the image interpolated by using the square interpolation mask has good continuity on the horizontal line, but not very good on the diagonal line. On the other hand, an image subjected to interpolation processing using a flat interpolation mask has good continuity of diagonal lines, but is not very good in continuity of horizontal lines. Accordingly, in the present embodiment, the pixel value D (Y) calculated using the square interpolation mask is compared with the pixel value D (Y) ′ calculated using the flat interpolation mask, and the pixel value is large. By adopting this value as the pixel value at the interpolation point Y, interpolation data is generated.

  Next, in step S14, the interpolation data generated in step S11 is used to perform interpolation processing on the local maximum data extracted in step S11. The image data generated thereby becomes structure image data representing an ultrasonic image (structure image) of the structure in the imaging region.

Next, in step S15, speckle image data representing the speckle image is generated by subtracting the value represented by the structure image data generated in step S14 from the value represented by the original data.
According to this speckle image generation method, by performing interpolation processing based on pixel values obtained by using a square interpolation mask and a flat interpolation mask, a structure image having good continuity in horizontal and oblique lines is obtained. be able to. Therefore, by using such a structure image, a speckle image with good separability from the structure can be obtained.

  As a modification of the speckle image generation method, interpolation data may be generated by using one of a square interpolation mask and a flat interpolation mask in steps S12 and S13 shown in FIG. When a square interpolation mask is used, a structure image with good continuity on the horizontal line can be acquired, and when a flat interpolation mask is used, a structure image with good continuity on the diagonal line can be acquired. Alternatively, when generating the interpolation data in step S13, the pixel value obtained by using the square interpolation mask and the pixel value obtained by using the flat interpolation mask are compared with those pixel values. It may be possible to select which one of the selected pixel values is used. Thereby, a user's desired structure image can be acquired.

Furthermore, the speckle image generation unit 41 in FIG. 1 may generate a speckle image using a two-dimensional mask process as described below. First and second speckle image generators 62 and 64 shown in FIG. 8, first and second speckle image generators 72 and 74 shown in FIG. 11, and first and second speckle image generators 72 and 74 shown in FIG. The same applies to the speckle image generation units 92 and 94.
FIG. 18 is a flowchart showing another speckle image generation method for generating a speckle image using two-dimensional mask processing. This speckle image generation method is performed on original data obtained by performing sector scanning, convex scanning, or radial scanning, and is characterized by using a two-dimensional mask process.

  In step S21 of FIG. 18, the speckle image generation unit 41 shown in FIG. 1 extracts a local maximum point in the original image based on the original data stored in the primary memory 27. Next, in step S22, the speckle image generation unit 41 generates interpolation data by calculating a pixel value of an interpolation point for interpolating the maximum point by four-point interpolation using a square interpolation mask. Note that the maximum point extraction process in step S21 and the pixel value calculation process in step S22 are the same as those described in steps S11 and S12 of FIG. 14 with reference to FIGS.

Next, in step S23, the interpolation data generated in step S22 is used to perform interpolation processing on the local maximum data extracted in step S21, and further, polar coordinate conversion processing (in accordance with the ultrasonic scanning method ( Scan conversion processing). Thereby, image data representing a sector image, a convex image, or a radial image is generated. Such image data becomes structure image data representing a structure image in the imaging region.

Next, in step S24, speckle image data representing the speckle image is generated by subtracting the value represented by the structure image data from the value represented by the original data.
According to this speckle image generation method, by performing interpolation processing and polar coordinate conversion based on the pixel value calculated by using a square interpolation mask with good continuity in the horizontal line, a structural image with good continuity can be obtained. Obtainable. Therefore, by using such a structure image, a speckle image with good separability from the structure can be obtained.

In the two speckle image generation methods described above, the pixel value of the interpolation point is calculated by performing four-point interpolation using the maximum value, but the pixel value is calculated using other methods. May be. For example, as shown in FIG. 19, pixel values of local maximum points Y ₁ , Y ₂ , Y ₃ ... Included in a predetermined range from the interpolation point Y (for example, inside a circle C1 centered on the interpolation point Y) are obtained. It may be used. In this case, the pixel value D (Y) of the interpolation point Y can be calculated by the following equation (1). In Expression (1), d ₁ , d ₂ , and d ₃ represent distances between the interpolation point Y and the maximum points Y ₁ , Y ₂ , and Y ₃ , respectively.
D (Y) = (1 / d ₁ ) (1 / d ₁ + 1 / d ₂ + 1 / d ₃ ) × Y ₁
+ (1 / d ₂ ) (1 / d ₁ + 1 / d ₂ + 1 / d ₃ ) × Y ₂
+ (1 / d ₃ ) (1 / d ₁ + 1 / d ₂ + 1 / d ₃ ) × Y ₃ (1)
According to this method, it is possible to shorten the time for searching for the pixel at the maximum point used when calculating the pixel value of the pixel Y.

Alternatively, the pixel value of the pixel Y may be calculated using Equation (2) or Equation (3) instead of Equation (1).
D (Y) = (1 / d ₁ ² ) (1 / d ₁ ² + 1 / d ₂ ² + 1 / d ₃ ² ) × Y ₁
+ (1 / d ₂ ² ) (1 / d ₁ ² + 1 / d ₂ ² + 1 / d ₃ ² ) × Y ₂
+ (1 / d ₃ ² ) (1 / d ₁ ² + 1 / d ₂ ² + 1 / d ₃ ² ) × Y ₃ (2)
D (Y) = (1 / d ₁ ³ ) (1 / d ₁ ³ + 1 / d ₂ ³ + 1 / d ₃ ³ ) × Y ₁
+ (1 / d ₂ ³ ) (1 / d ₁ ³ + 1 / d ₂ ³ + 1 / d ₃ ³ ) × Y ₂
+ (1 / d ₃ ³ ) (1 / d ₁ ³ + 1 / d ₂ ³ + 1 / d ₃ ³ ) × Y ₃ (3)
When using formula (2) or formula (3), it becomes more susceptible to the maximum point close to the pixel Y than when using formula (1).

  In the two speckle image generation methods described above, the structure image data is generated by using the local maximum point in the original image. Instead of the local maximum point, the local minimum point, the local maximum point and the local minimum point are used. An average point between them may be used.

Further, the speckle image generation unit 41 illustrated in FIG. 1 may perform frequency band division processing as described below on the speckle image data and the structure image data. First and second speckle image generators 62 and 64 shown in FIG. 8, first and second speckle image generators 72 and 74 shown in FIG. 11, and first and second speckle image generators 72 and 74 shown in FIG. The same applies to the speckle image generation units 92 and 94.
Note that the frequency band division processing here refers to image processing that divides an ultrasonic image into a plurality of frequency bands and emphasizes a desired frequency component.

As shown in FIG. 20, speckle image data or structure image data (hereinafter referred to as “image data DT (0)”) generated by the speckle image generation unit 41 is thinned by the downsampling unit 701. At the same time, the thinned data is subjected to filter processing such as Nyquist filter processing. By repeating such processing, downsampling data DT (1), DT (2),..., DT (N) having low spatial frequency components are sequentially generated.

  Next, in the upsampling unit 702, 0-value data is inserted into the nth downsampled data DT (n) (n = 1 to N), and filter processing such as smoothing filter processing is performed. As a result, upsampling data DT (n) ′ having the same size as the adjacent (n−1) th data is obtained.

Next, in the subtraction unit 703, a subtraction process is performed between the (n-1) th down-sampling data DT (n-1) and the adjacent n-th upsampling data DT (n) '. Thereby, the subtraction data DS (0) to DS (N-1) are obtained. These subtraction data DS (0) ~DS (N- 1) , the data group including image data DT (0) frequency components of the spatial frequency components _f 0 ~f _N is divided into N frequency bands included in each It is. For example, the subtraction data DS (n) (n = 0 to N−1) includes frequency components f _{n to} f _{n + 1} .

Then, the multiplying unit 704, subtraction data DS (0), DS (1 ), ..., the DS (N-1), the weighting coefficients _{k 0, k 1, ...,} k N-1 are multiplied respectively. Further, the data DS (n) ′ (n = 1 to N−1) multiplied by the weighting factor is upsampled by the upsampling unit 705 so that the data size is equal to the original image data DT (0). The

The data DS (0) and DS (1) ′, DS (2) ′,..., DS (N−1) ′ having the same data size are added in the adding unit 706. Thus, the data DT _EN weighted for each spatial frequency band is generated. Furthermore, the weighted data _{DT EN} and the original image data DT (0), in the multiplication section 707, predetermined weighting coefficients _{K FR} and _{(1-K FR)} is multiplied respectively, are summed in adder unit 708 . In this manner, the frequency-enhanced image data DT _OUT is generated and output to the speckle image analysis unit 42 and the image selection unit 30 (FIG. 1).

The weighting coefficients k _{0 to} k _N−1 used in the multiplication unit 704 are set according to the characteristics of the image data to be processed. The weighting coefficients k _{0 to} k _N−1 may be recorded in advance in the recording unit 36 shown in FIG. 1 in association with parameters such as the ultrasonic frequency, the depth of the subject, and the observation site. The user may input an arbitrary value. In the former case, an appropriate weighting coefficient is set according to those parameters, and in the latter case, a user-desired frequency enhancement effect can be obtained.

When such frequency band division processing is applied to speckle image data, a large size speckle is reduced by reducing the weighting coefficients k ₄ and k ₅ and reducing components having relatively low frequencies. Is done. Thereby, in the synthesized image, it is possible to obtain an image in which the tissue part is particularly easy to see. On the other hand, when such frequency band division processing is performed on the structure image data, the vicinity of the structure is enhanced by enlarging the weighting coefficients k ₀ and k ₁ and emphasizing components having relatively high frequencies. A clear effect can be obtained.

Further, the speckle image generation unit 41 illustrated in FIG. 1 may perform various image processing in addition to such frequency band division processing. Specifically, smoothing filter processing, Laplacian filter processing, and the like can be given. First and second speckle image generators 62 and 64 shown in FIG. 8, first and second speckle image generators 72 and 74 shown in FIG. 11, and first and second speckle image generators 72 and 74 shown in FIG. The same applies to the speckle image generation units 92 and 94.

  INDUSTRIAL APPLICABILITY The present invention can be used in an ultrasonic diagnostic apparatus used for medical treatment or nondestructive inspection of structures.

FIG. 1 is a block diagram showing an ultrasonic observation apparatus according to an embodiment of the present invention. It is a block diagram which shows the structure of the speckle image analysis part 42 of the speckle analysis part 29 shown in FIG. It is a figure which shows the original image represented by original data. FIG. 4 is a diagram illustrating original data regarding a region indicated by a one-dot chain line A-A ′ in FIG. 3. It is a figure for demonstrating the method to extract structure image data. It is a figure showing the speckle image and structure image which were isolate | separated from the original image. It is a figure showing the mixed image of a speckle image and a structure image. It is a block diagram which shows the other structure of the speckle analysis part 29 shown in FIG. It is a figure for demonstrating the frequency characteristic of LPF61 and HPF63 shown in FIG. It is a figure for demonstrating the frequency process in the frequency process part 66 shown in FIG. It is a block diagram which shows the other structure of the speckle analysis part 29 shown in FIG. It is a block diagram which shows the other structure of the speckle analysis part 29 shown in FIG. It is a block diagram which shows the other structure of the speckle analysis part 29 shown in FIG. It is a flowchart which shows an ultrasonic image processing method. It is a figure for demonstrating the method of extracting the local maximum point of a pixel. It is a figure for demonstrating the method of performing 4-point interpolation using a square interpolation mask. It is a figure for demonstrating the method of performing 4-point interpolation using a flat interpolation mask. It is a flowchart which shows the other ultrasonic image processing method. It is a figure for demonstrating another method for calculating the pixel value of an interpolation point. It is a figure for demonstrating a frequency band division process.

Explanation of symbols

DESCRIPTION OF SYMBOLS 10 Ultrasonic probe 20 Control part 21 Scan control part 22 Drive signal generation part 23 Transmission / reception switching part 24 Signal processing part 25 A / D converter 26 Phase matching part 27 Primary memory 29 Speckle analysis part 30 Image switching part 31 DSC
32 Secondary memory 33 D / A converter 34 Display unit 35 Console 36 Recording unit 41, 62, 64, 72, 74, 82, 84, 92, 94 Speckle image generation unit 42 Speckle image analysis unit 44 Speckle Analysis mode button 45 Image selection unit control button 46 DSC control button 51, 65, 77, 85, 87, 98 Switch 52, 53 Speckle image memory 54, 90, 97, 101 Difference calculation unit 61, 71, 81, 91 LPH
63, 73, 83, 93 HPF
66, 75, 76, 86, 95, 96 Frequency processing unit 88, 89, 99, 100 Frequency processing memory

Claims (31)

Reception processing means for generating original data by processing a reception signal obtained based on an ultrasonic echo generated by reflection of an ultrasonic beam transmitted from an ultrasonic transducer from a subject;
Generating speckle image data based on the original data generated by the reception processing means, and speckle analysis means for analyzing the speckle image represented by the generated speckle image data;
Display means for displaying an analysis result in the speckle analysis means;
An ultrasonic observation apparatus comprising:   The speckle analysis means is represented by speckle image generation means for generating speckle image data based on the original data generated by the reception processing means, and speckle image data generated by the speckle image generation means. The ultrasonic observation apparatus according to claim 1, further comprising speckle image analysis means for performing analysis of the speckle image to be generated and generating speckle analysis result image data indicating an analysis result.   The speckle analysis means generates speckle image data based on the original data generated by the reception processing means, and speckle image data input from the speckle image generation means Switch means for switching every time and alternately outputting from the first and second output terminals, and storing speckle image data for one frame inputted from the first and second output terminals of the switch means, respectively. And calculating a difference between the value of the speckle image data stored in the first storage unit and the value of the speckle image data stored in the second storage unit. The ultrasonic observation apparatus according to claim 1, further comprising difference calculation means.   The speckle analysis means generates at least two speckle image data based on different frequency band components of the original data generated by the reception processing means, and the at least two speckle image generations. A switching means for selecting and outputting one of speckle image data input from the means; and a frequency processing means for performing frequency processing on the speckle image data input from the switching means. 1. The ultrasonic observation apparatus according to 1.   The speckle analysis unit generates the first speckle image data based on the low frequency component of the original data generated by the reception processing unit, and the reception processing unit generates the speckle image data. Second speckle image generation means for generating second speckle image data based on the high-frequency component of the original data, and the first speckle image input from the first speckle image generation means First frequency processing means for performing frequency processing on data, and second frequency processing means for performing frequency processing on second speckle image data input from the second speckle image generation means The value of the first speckle image data subjected to frequency processing from the first frequency processing means and the frequency processing from the second frequency processing means are applied. And a difference calculating means for calculating a difference between the value of the second speckle image data, the ultrasonic observation apparatus according to claim 1. The speckle analysis means generates at least two speckle image data based on different frequency band components of the original data generated by the reception processing means, and the at least two speckle image generations. First switch means for selecting and outputting one of speckle image data input from the means, frequency processing means for performing frequency processing on speckle image data input from the first switch means, Second switch means for switching the speckle image data input from the frequency processing means for each frame and outputting it to two storage units; and one frame of the value of the speckle image data stored in the two storage units The ultrasonic observation apparatus according to claim 1, further comprising difference calculation means for calculating a difference for each.   The speckle analysis unit generates the first speckle image data based on the low frequency component of the original data generated by the reception processing unit, and the reception processing unit generates the speckle image data. Second speckle image generation means for generating second speckle image data based on the high-frequency component of the original data, and the first speckle image input from the first speckle image generation means First frequency processing means for performing frequency processing on data, and second frequency processing means for performing frequency processing on second speckle image data input from the second speckle image generation means The first speckle image data value subjected to frequency processing by the first frequency processing means and the frequency processing performed by the second frequency processing means. First difference calculation means for calculating a difference from the value of the second speckle image data and generating speckle image difference data; and speckle image difference data output from the first difference calculation means. Switch means for switching each frame and outputting it to the first and second storage units, the value of the speckle image difference data stored in the first storage unit, and the speckles stored in the second storage unit The ultrasonic observation apparatus according to claim 1, further comprising: a second difference calculation unit that calculates a difference from the value of the image difference data.   The frequency processing means performs a waveform / frequency conversion process on the speckle signal for one line for a predetermined window width section, and the integrated value of the frequency components obtained by the waveform / frequency conversion process is used as the center of the window width. The ultrasonic observation according to any one of claims 4 to 7, wherein the processing for obtaining the representative value for one line by changing the window width is performed on the speckle signals for all lines. apparatus.   The speckle image generation means interpolates between the maximum point or the minimum point extracted in the original data, the average point of the maximum point and the minimum point, and between the maximum points, between the minimum points, or between the average points. Generate structure image data based on at least one of the obtained interpolation points, and generate speckle image data by subtracting the value represented by the structure image data from the value represented by the original data The ultrasonic observation apparatus according to any one of claims 2 to 7.   The speckle image generation means divides the ultrasonic image into four two-dimensional regions with reference to the interpolation point, and the four local maximum points or local minimum points or the average point between the local maximum points and the local minimum points selected in each region The ultrasonic observation apparatus according to claim 9, wherein a pixel value of the interpolation point is obtained by performing a four-point interpolation process using the.   The ultrasonic observation apparatus according to claim 9 or 10, wherein the speckle image generation means selects the four maximum points, minimum points, or average points by using a square interpolation mask.   The ultrasonic observation apparatus according to claim 9 or 10, wherein the speckle image generation means selects four maximum points, minimum points, or average points by using a flat interpolation mask.   The speckle image generation means has a pixel value based on four local maximum points or local minimum points or average points selected by using a square interpolation mask, and four local maximum points or local minimums selected by using a flat interpolation mask. The ultrasonic observation apparatus according to claim 9 or 10, wherein a pixel value of the interpolation point is obtained by comparing a pixel value based on a point or an average point.   The speckle image generation means is a pixel value of a maximum point, a minimum point, or an average point extracted from the original data, a distance between the point and the interpolation point, a square of the distance, or 3 of the distance. The ultrasonic observation apparatus according to claim 9, wherein a pixel value of the interpolation point is obtained based on the power.   The ultrasonic observation apparatus according to claim 1, further comprising operation means having a speckle analysis mode button for operating the speckle analysis means. An image selecting means for selecting one of the speckle image generated by the speckle image generating means and the speckle image analysis result image generated by the speckle analyzing means;
The operation means has a control button for causing the image selection means to select one of the speckle image and the speckle image analysis result image by an operator.
The ultrasonic observation apparatus according to claim 15. Further comprising a digital scan converter means for generating a parallel image in which the original image represented by the original data and the speckle image analysis result image generated by the speckle analysis means are arranged in different sizes,
The operation means further includes another control button that allows the digital scan converter means to select an size of the original image and the speckle image analysis result image.
The ultrasonic observation apparatus according to claim 15 or 16. Speckle that generates speckle image data based on original data generated by processing a received signal obtained based on an ultrasonic echo generated by reflection of an ultrasonic beam transmitted from an ultrasonic transducer from a subject. Image generating means;
Speckle image analysis means for analyzing a speckle image represented by speckle image data generated by the speckle image generation means;
An image processing apparatus comprising:   The speckle image analysis means performs analysis of a speckle image represented by speckle image data generated by the speckle image generation means, and generates speckle analysis result image data indicating an analysis result. Item 19. The image processing apparatus according to Item 18.   The speckle image analysis means is input from switch means for switching speckle image data for each frame and alternately outputting from the first and second output terminals, and from the first and second output terminals of the switch means. First and second storage units for storing speckle image data for one frame respectively, and values of speckle image data stored in the first storage unit and stored in the second storage unit The image processing apparatus according to claim 18, further comprising difference calculation means for calculating a difference from the value of the speckle image data that has been set. Comprising at least two speckle image generation means for generating speckle image data based on different frequency band components of the original data;
The speckle image analysis means selects and outputs one of the speckle image data input from the at least two speckle image generation means, and the speckle image data input from the switch means 19. An image processing apparatus according to claim 18, further comprising frequency processing means for performing frequency processing. First speckle image generation means for generating first speckle image data based on the low frequency component of the original data, and second speckle image data based on the high frequency component of the original data Second speckle image generation means,
The first speckle image analysis means performs first frequency processing on the first speckle image data input from the first speckle image generation means, and the second speckle image. Second frequency processing means for performing frequency processing on the second speckle image data input from the generating means, and first speckle image data subjected to frequency processing from the first frequency processing means The image processing apparatus according to claim 18, further comprising: a difference calculating unit that calculates a difference between the value of the second speckle image data subjected to frequency processing from the second frequency processing unit. . Comprising at least two speckle image generation means for generating speckle image data based on different frequency band components of the original data;
The speckle image analysis means selects and outputs one of speckle image data input from the at least two speckle image generation means, and is input from the first switch means. Frequency processing means for performing frequency processing on speckle image data; second switch means for switching speckle image data input from the frequency processing means for each frame and outputting the same to two storage units; The image processing apparatus according to claim 18, further comprising difference calculation means for calculating a difference for each frame of the values of the speckle image data stored in the two storage units. First speckle image generation means for generating first speckle image data based on the low frequency component of the original data, and second speckle image data based on the high frequency component of the original data Second speckle image generation means,
The first speckle image analysis means performs first frequency processing on the first speckle image data input from the first speckle image generation means, and the second speckle image. Second frequency processing means for performing frequency processing on the second speckle image data input from the generating means, and first speckle image data subjected to frequency processing by the first frequency processing means. First difference calculation means for generating speckle image difference data by calculating a difference between the value and the value of the second speckle image data subjected to frequency processing by the second frequency processing means; Switch means for switching the speckle image difference data output from one difference calculation means for each frame and outputting it to the first and second storage sections; and the switch means stored in the first storage section. The image processing according to claim 18, further comprising second difference calculation means for calculating a difference between a value of speckle image difference data and a value of speckle image difference data stored in the second storage unit. apparatus.   Speckle image data is generated based on the original data generated by processing the received signal obtained by the computer based on the ultrasonic echo generated when the ultrasonic beam transmitted from the ultrasonic transducer is reflected from the subject. An image processing program for functioning as a speckle image generation unit that performs analysis of a speckle image represented by speckle image data generated by the speckle image generation unit.   The speckle image analysis means performs analysis of a speckle image represented by speckle image data generated by the speckle image generation means, and generates speckle analysis result image data indicating an analysis result. Item 25. The image processing program according to Item 25.   The speckle image analysis means is input from switch means for switching speckle image data for each frame and alternately outputting from the first and second output terminals, and from the first and second output terminals of the switch means. First and second storage units for storing speckle image data for one frame respectively, and values of speckle image data stored in the first storage unit and stored in the second storage unit 26. The image processing program according to claim 25, further comprising difference calculation means for calculating a difference from the value of the speckle image data that has been set. The speckle image analysis means selects and outputs one of the speckle image data input from at least two speckle image generation means for generating speckle image data based on different frequency band components of the original data. 26. The image processing program according to claim 25, further comprising: a switching unit; and a frequency processing unit that performs frequency processing on speckle image data input from the switching unit.   For the first speckle image data input from the first speckle image generation means for generating the first speckle image data based on the low frequency component of the original data. Second speckle image input from first frequency processing means for performing frequency processing and second speckle image generation means for generating second speckle image data based on the high frequency component of the original data A second frequency processing means for performing frequency processing on the data, a value of the first speckle image data subjected to the frequency processing from the first frequency processing means, and the second frequency processing means. 26. The image processing program according to claim 25, further comprising difference calculation means for calculating a difference from the value of the second speckle image data subjected to frequency processing.   The speckle image analysis means selects and outputs one of the speckle image data input from at least two speckle image generation means for generating speckle image data based on different frequency band components of the original data. First switching means, frequency processing means for performing frequency processing on speckle image data input from the first switching means, and switching of speckle image data input from the frequency processing means for each frame And a second switch means for outputting to the two storage sections, and a difference calculation means for calculating a difference for each frame of the value of the speckle image data stored in the two storage sections. 25. The image processing program according to 25.   For the first speckle image data input from the first speckle image generation means for generating the first speckle image data based on the low frequency component of the original data. Second speckle image input from first frequency processing means for performing frequency processing and second speckle image generation means for generating second speckle image data based on the high frequency component of the original data Second frequency processing means for performing frequency processing on the data, value of the first speckle image data subjected to frequency processing by the first frequency processing means, and frequency processing by the second frequency processing means A first difference calculating means for calculating a difference from the value of the second speckle image data subjected to the processing to generate speckle image difference data; and the first difference calculating means. Switching means for switching the speckle image difference data output from each frame and outputting it to the first and second storage units, the value of the speckle image difference data stored in the first storage unit, and the first 26. The image processing program according to claim 25, further comprising second difference calculation means for calculating a difference from the value of the speckle image difference data stored in the second storage unit.