JP5205152B2 - Ultrasonic image processing apparatus and program - Google Patents

Description

The present invention relates to an ultrasonic image processing apparatus and program, particularly, by using the phase information included in the ultrasound image data to extract the speckle noise, analyzing the tissue characterization from a density of the extracted speckle noise Regarding technology.

  2. Description of the Related Art Conventionally, there has been widely known an ultrasonic diagnostic imaging apparatus that acquires a tomographic image of a subject using ultrasonic waves and uses it for medical diagnosis. The ultrasonic diagnostic imaging apparatus transmits an ultrasonic wave to a subject, and based on an echo signal (luminance information) obtained by the ultrasonic wave reflected and received from the subject, an image of the tomography of the subject (B-mode image) is generated, and the tomographic image is displayed on the display means.

  At this time, in general ultrasonic diagnosis, the tissue property is determined by the B-mode image, that is, the luminance information. However, the image (black and white image) obtained from the luminance information greatly changes the impression of image quality such as contrast depending on the image processing and parameters of the apparatus, so that an optimal image adjustment is necessary to diagnose objective tissue properties. In addition to knowledge about the device, it was very difficult to adjust appropriately for each patient.

  On the other hand, for example, Patent Document 1 proposes a method of extracting speckle components from an ultrasonic image by threshold processing or the like and analyzing the tissue properties from the speckle density. Here, speckle noise in an ultrasonic image refers to black streak-like noise generated by random interference of reflected ultrasonic waves from a plurality of reflectors in a subject. In the above-mentioned Patent Document 1, a portion other than black streaks is defined as speckle noise, assuming that a slightly bright portion (particles or island-like regions) on the B-mode image corresponds to speckle. Even if speckle noise is defined in this way, the analysis method and problem are the same.

According to the invention described in Patent Document 1, since there is a correlation between the speckle pattern and the tissue property and depends on the tissue property, objective diagnostic information can be obtained by evaluating the speckle density. It can be expected to be obtained.
JP 2006-212445 A

  However, since the speckle noise shape and the echo level vary and are very complicated in the one described in Patent Document 1, it is very difficult to accurately extract the speckle noise portion from the amplitude information. However, there was a problem that the analysis of the tissue properties was difficult.

The present invention has been made in view of such circumstances, and aims to provide not affected by the variations in the amplitude, the ultrasonic imaging apparatus and a program that allows analysis of accurate tissue characterization To do.

In order to achieve the above object, an ultrasonic image processing apparatus of the present invention transmits ultrasonic waves toward a subject and receives ultrasonic waves reflected from the subject; The speckle component extraction means for extracting the speckle component from the phase component using the amount of change in the phase component of the ultrasonic signal received by the ultrasonic transmission / reception means, and the density of the extracted speckle component, An analysis means for analyzing the tissue properties included in the sound wave image .

This makes it possible to accurately analyze the tissue properties without being affected by variations in amplitude. Moreover, speckle extraction accuracy can be improved.

Moreover , it has a display means which displays the result of having analyzed the said tissue property.

  Thereby, the accuracy of the ultrasonic image diagnosis is improved.

Further , the speckle component extracting means uses a change amount of at least two directions of the phase component.

  Thus, the accuracy of speckle extraction can be improved by using the amount of change in a plurality of directions.

Further , the speckle component extracting means uses a discriminant function for discriminating speckle.

  Thereby, a discriminant function according to the purpose of speckle extraction can be used.

Further , the speckle component extraction means uses a threshold value for discriminating speckle in comparison with the change amount of the phase component.

  This facilitates speckle extraction when speckle can be determined by comparison with a threshold value.

Further , the discriminant function is set by a feature amount generated from phase data in which speckle and non-speckle positions are known.

  Thereby, a discriminant function can be created using the feature amount according to the speckle extraction process.

Further , the density of the extracted speckle component is converted into a ratio of a speckle region in a local region.

Further , the analyzing means uses reference information in which the density of speckle components and the tissue properties are associated with each other.

Similarly, in order to achieve the above object, the present invention provides an ultrasonic transmission / reception means for transmitting an ultrasonic wave toward a subject and receiving an ultrasonic wave reflected from the subject, and the ultrasonic wave Using the speckle component extracting means for extracting the speckle component from the combination of the amplitude component or envelope component and phase component of the ultrasonic signal received by the transmitting / receiving means, and the density of the extracted speckle component, an ultrasonic image is obtained. And an analysis means for analyzing the tissue properties contained in .

  As a result, it is possible to analyze the tissue property more accurately by combining the amplitude information with the phase information.

Further , the image processing means is provided with image processing means for adjusting the image quality of the output image using the analyzed tissue property data.

  Thereby, the accuracy of image diagnosis can be improved.

Further , the tissue property output from the analyzing means is a sound velocity of the tissue.

  Thereby, the sound speed of a finer area can be known, and a higher-accuracy image can be generated using this.

In addition, the phase component, and wherein the resolution of the data in the direction perpendicular to the traveling direction of the ultrasonic signal, the at least sequence distance between the device for transmitting and receiving the ultrasonic signal of the ultrasonic transmission reception means To do.

  Thereby, resolution | decomposability improves and a tissue property analysis can be performed more correctly.

Further , it is characterized in that images with different sound speeds are generated from a single received data.

  As a result, it is possible to generate an image with various ways of obtaining the sound speed, and to improve the resolution.

In addition , an image in which the sound speed is locally changed is generated using the analyzed tissue property data.

  Thereby, analysis with higher accuracy becomes possible.

Similarly, in order to achieve the above object, the ultrasonic image processing program of the present invention transmits an ultrasonic wave toward a subject and receives an ultrasonic wave reflected from the subject, Analyzes the tissue properties included in the ultrasound image using the function of extracting speckle components from the phase components using the amount of phase component change of the received ultrasonic signal and the density of the extracted speckle components. And a function to be realized by a computer .

  This makes it possible to accurately analyze the tissue properties without being affected by speckle noise.

  As described above, according to the present invention, it is possible to accurately analyze tissue properties without being affected by variations in amplitude.

  Hereinafter, an ultrasonic image processing apparatus, method, and program according to the present invention will be described in detail with reference to the accompanying drawings.

  FIG. 1 is a block diagram showing a schematic configuration of an embodiment of an ultrasonic diagnostic apparatus including an ultrasonic image processing apparatus according to the present invention. This ultrasonic diagnostic apparatus captures and displays an ultrasonic image of a diagnostic region of a subject using ultrasonic waves, and mainly includes an ultrasonic probe 10, a signal processing unit 20, and image processing. The unit 30 and the display unit 40 are included.

  The ultrasonic probe 10 transmits ultrasonic waves toward a diagnosis site in the body of the subject and receives ultrasonic waves reflected in the body of the subject. In other words, since ultrasonic waves are reflected at the boundary between regions having different acoustic impedances, such as the boundary between structures, an ultrasonic echo generated within the subject by transmitting an ultrasonic beam into the subject such as a human body. The contour of the structure existing in the subject can be detected by obtaining the reflection point and reflection intensity at which the ultrasonic echo is generated.

  The ultrasonic probe 10 includes, for example, a plurality of ultrasonic transducers constituting a one-dimensional ultrasonic transducer array, and each ultrasonic transducer is formed by a vibrator in which electrodes are formed at both ends of a piezoelectric element such as PZT. Although configured, it is not particularly limited. For example, in addition to a linear array probe in which a plurality of ultrasonic transducers are arranged one-dimensionally in this way, a sector probe that scans a subject in a fan shape, and a convex array probe in which a plurality of ultrasonic transducers are arranged on a convex surface Alternatively, a two-dimensional array probe in which a plurality of ultrasonic transducers are two-dimensionally arranged may be used. Alternatively, it may be a mechanical radial probe that performs radial scanning in an ultrasonic endoscope.

  The ultrasound probe 10 transmits an ultrasound beam to the subject under the control of a control unit (not shown), and is scanned by a scanning method such as linear scanning, sector scanning, convex scanning, or radial scanning. Scan the examiner. The ultrasonic waves generated by the ultrasonic probe 10 are reflected by a reflector present in the body of the subject, and the reflected ultrasonic waves are received by the ultrasonic probe 10. The ultrasonic reception signal received by the ultrasonic probe 10 is converted into an electric signal and then delivered to the signal processing unit 20 via a transmission / reception unit (not shown).

  The signal processing unit 20 performs predetermined signal processing on the input received signal, phase matching addition for adding a plurality of received data with delay from the received ultrasonic received signal, and the received data as IQ data (complex Signal) to generate IQ data and output it to the image processing unit 30.

  Note that the amplitude A and phase θ of the received data are calculated from the IQ data by the following equations.

A = √ (I ² + Q ² ), θ = arctan (Q / I)
Here, the symbol √ (X) represents the square root of X. Further, when taking the envelope of the waveform of the received data, the amplitude must be 2A by doubling the above value.

  The signal processing unit 20 may use a method of generating data for each line. For example, as described in Japanese Patent Application Laid-Open No. 2003-180688, the reception data of all elements is stored in a memory, A method of generating data by processing may be used. With this method, the azimuth resolution (element direction resolution) can be further improved. For example, high-resolution phase information is used in the direction perpendicular to the traveling direction of the ultrasonic signal so that the resolution of the phase information in the direction perpendicular to the traveling direction of the ultrasonic signal is larger than the element arrangement interval. Thus, the random phase change of speckle can be more accurately distinguished.

  Further, in a general ultrasonic B-mode image, an image is generated from amplitude and phase information is not used. Therefore, ultrasonic image data in the existing ultrasonic image processing technique (B mode) indicates amplitude data.

  Next, the relationship between signal interference and phase will be described with reference to the drawings. Interference includes constructive interference and canceling interference. Constructive interference occurs when the phase difference between the waves is small, and destructive interference occurs when the phase difference is close to π.

  FIG. 2 shows an example of constructive interference. FIG. 2A shows the state before interference, and three waves with small phase differences of 0.2 (rad), 0.4 (rad), and 0.6 (rad) are used for the reference wave. To interfere. FIG. 2B shows the state after interference, and the interference wave is represented by a solid line with respect to the reference wave represented by a broken line. When the phase difference is small in this way, an intensifying interference wave is obtained. As can be seen from FIG. 2B, the peak of the peak of the interference wave is close to the peak of the peak of the reference wave, and the interference wave having a small phase difference has a small phase difference from the reference wave even after the interference.

  FIG. 3 shows an example of destructive interference. FIG. 3A shows the state before interference. In this case, a phase difference of 3.0 (rad) and a phase difference of 3.2 (rad) in addition to the phase difference of 0.2 (rad) with respect to the reference wave. Is close to π (rad), causing large waves to interfere. At this time, as shown in FIG. 3B, a destructive interference wave is obtained as represented by a solid line with respect to a reference wave represented by a broken line. When waves having such a large phase difference are caused to interfere with each other, destructive interference waves are obtained. As can be seen from FIG. 3B, the peak of the peak of the interference wave is separated from the peak of the peak of the reference wave, and the interference wave having a large phase difference has a large phase difference from the reference wave even after the interference. .

  Previously, speckle noise was said to be caused by random interference of reflected ultrasound from multiple reflectors in the subject, but interference with a large phase difference as shown in FIG. 3 generates speckle noise. Interference.

  Further, the phase obtained from the IQ data represents the phase difference between the wave after interference and the wave after detection. Since the phase of IQ detection includes an error, it is a result of interference having a large phase difference from the wave after interference. In order to confirm whether the result is small interference, it is only necessary to look at a continuous phase change of IQ data (for example, a difference between adjacent pixels). This is because when a wave having a small phase difference continues, the phase change is small, and when the speckle noise portion is reached, the phase changes suddenly and the phase change increases.

  In this way, speckle noise can be determined by using the feature that the phase changes suddenly.

  The image processing unit 30 performs image processing on the IQ data obtained by the signal processing unit 20 and displays the IQ data on the display unit 40. The center of processing in the image processing unit 30 is to extract phase information from IQ data of the signal processing unit 20 to determine speckles and extract speckle components.

  FIG. 4 shows the flow of speckle extraction processing using phase information in the image processing unit 30.

  As illustrated in FIG. 4, the image processing unit 30 includes a speckle extraction unit (speckle component extraction unit) 35 including a phase change amount extraction unit 32 and a speckle determination unit 34.

  Phase data is input to the phase change amount extraction unit 32 from the IQ data obtained by the signal processing unit 20. As described above, the phase is given by the equation θ = arctan (Q / I). The phase change amount extraction unit 32 extracts the phase change amount from the phase data. The extracted phase change amount data is input to the speckle determination unit 34. The speckle determination unit 34 extracts a speckle component using the phase change amount data and outputs a speckle extraction result.

  FIG. 5 shows an example of processing in the phase change amount extraction unit 32.

  When the phase change amount extraction unit 32 obtains the phase data, first, the phase change amount in the azimuth direction (lateral direction) and the phase change amount in the distance direction (depth direction), that is, the pixel difference (azimuth direction difference) in each direction. And the distance direction difference).

  In the case of a two-dimensional B-mode image, it is desirable to determine the amount of phase change in the azimuth direction and the distance direction in this way. This is because when speckle noise exists parallel to either the azimuth direction or the distance direction, the phase change is small in that direction, but the phase change is large in the direction orthogonal thereto.

  Next, a certain amount of phase change due to detection deviation is removed. Note that the speckle phase change changes abruptly compared to other parts, so only the components that increase the amount of change, such as differential data from the filter (low-pass filter, median filter) and secondary differentiation of the phase data, are extracted. By doing so, it is possible to obtain data having characteristics that make it easier to discriminate speckle.

  Then, a component whose phase changes suddenly in each direction is extracted, and phase change amount data in each direction is calculated. The amount of change is obtained from the difference between pixels in each direction. At this time, it is most preferable to use a difference between adjacent pixels, but a difference between pixels thinned out as appropriate may be used.

  In this manner, speckle / signal determination can be performed based on information obtained by removing a fixed amount of phase change in each of the vertical and horizontal directions.

  Note that the phase change amount is not limited to this information, and a difference average of a plurality of pixels or a difference in an oblique direction may be used. Furthermore, instead of separate change amounts, the data may be converted into one data such as a vector component.

  In the speckle discrimination unit 34, the obtained phase change data is subjected to binary discrimination as to whether it is speckle noise or multi-level discrimination as to how much speckle noise is included. Results are output according to purpose. Of course, the phase change amount itself may be output as data indicating speckle-likeness.

  The azimuth direction phase change amount data and the distance direction phase change amount data extracted by the phase change amount extraction unit 32 are input to the speckle determination unit 34. The speckle discriminating unit 34 discriminates speckles based on these phase information data.

  FIG. 6 shows a configuration example of the speckle determination unit 34.

  As shown in FIG. 6, the speckle discrimination unit 34 includes a discrimination function creation unit 341. The discriminant function creating unit 341 creates a discriminant function for discriminating whether or not speckle noise is generated from the phase change amount data.

  The discriminant function creating unit 341 prepares in advance phase data in which the speckle or non-speckle position is known in the amplitude image, and creates a discriminant function based on the feature amount created from the phase change amount. That is, a predetermined feature amount is calculated in the feature amount conversion unit 342 from the phase change amount data 344a known to be speckle and the phase change amount data 344b known to be non-speckle, From this, a speckle discriminant function is created.

  Here, the feature amount may be a single pixel of phase change amount data. However, in the case of a single pixel, when the phase change amount is obtained by taking the difference, a slight pixel shift occurs. When the phase change amount is obtained from the vertical and horizontal directions, the phase of the center of the crossing portion is obtained. Since there is a problem that the change does not increase, it is desirable to use a plurality of data such as the value of the neighboring pixel of the target pixel and the calculation result with the neighboring pixel. At this time, since a plurality of data are multidimensional, the dimension may be lowered by performing PCA (principal component analysis) or the like so that the threshold value can be easily designed.

  FIG. 7 shows an example of the speckle discriminant function.

  Here, the vertical direction phase change amount and the horizontal direction phase change amount are defined as a feature amount (1) and a feature amount (2), respectively, non-speckle noise is represented by ◯, and speckle noise is represented by x. In the example shown in FIG. 7, the discriminant function is set as a straight line that separates the non-speckle noise O and speckle noise x regions. A function (or threshold) for discriminating between speckle noise and non-speckle noise is designed based on the result converted into the feature quantity in this way. The discriminant function is not limited to such a linear function.

  The discriminant function setting method is not particularly limited. For example, a statistical method using known data (learning data) such as SVM (support vector machine) (for example, Nero Christianini as a reference). And a linear or non-linear discriminant function used for known classification such as “Introduction to Support Vector Machine” by John Taylor, etc.). Of course, speckle may be determined only by the threshold value if it can be determined only by giving a threshold value for each feature amount. In addition, a discrimination function may be set with data representing a phase change, such as pixels of continuous phase data, as a feature quantity without being converted into a phase change amount.

  FIG. 8 shows an example of a speckle extraction discriminant function generation process in the feature quantity conversion unit 342 using SVM (support vector machine).

  As shown in FIG. 8, first, speckle portions and non-speckle portions are manually labeled from a phantom image, and known data for creating a speckle discriminant function is created. Note that when labeling speckles and non-speckle parts, labeling is not performed for ambiguous parts. Next, a feature amount used for discrimination of speckle is extracted from the speckle portion and the non-speckle portion of the known data.

  Here, as a feature amount, as shown in FIG. 9, a central pixel c of 3 × 3 pixels is set as a target pixel, and the target pixel c and four neighboring pixels a, b, d, and e above, below, left, and right are vertically A total of ten feature quantities, ie, (distance) direction phase change and lateral (azimuth) direction phase change are used.

  Next, a discriminant function (speckle discrimination) is applied by applying SVM (support vector machine) with the phase change amount of a total of 10 positions of the labeled pixels (the target pixel and its vicinity) in the vertical direction and the horizontal direction as feature amounts. Generator). Of course, the data and feature quantities used for labeling may be changed so that the discrimination result is optimal.

  As described above, the discriminant function creation unit 341 creates the speckle discriminant function by extracting the feature amount from the phase data in which the speckle or non-speckle position is known in advance. In actual ultrasonic diagnosis, speckle extraction is performed based on phase information obtained from IQ data generated by the signal processing unit 20 from data obtained by scanning the ultrasonic probe 10.

  That is, in FIG. 6, when the azimuth (transverse) direction phase change amount data 346a and the distance (vertical) direction phase change amount data 346b are input, the feature amount conversion unit 343 converts them into feature amounts used for speckle discrimination. Then, using the speckle discriminant function 347 created in advance by the discriminant function creating unit 341, it is discriminated whether it is speckle, and speckle extraction is performed. Then, the speckle extraction result 348 is output and displayed on the display unit 40. In this display, only speckles may be displayed or may be displayed so as to be superimposed on the amplitude image that is the original image.

  The discrimination result may not only indicate whether the speckle is binary, but also output the difference from the threshold as speckle-likeness indicating how much speckle noise is included in multiple values. . In the case of multi-value output, the value may be further adjusted using a LUT (Look Up Table) or the like.

  Note that the speckle discriminant function changes depending on the condition of ultrasonic transmission / reception, and therefore, in the case of an actual apparatus, it is desirable to set the discriminant function for each condition.

  In the example described above, speckle components are extracted using phase information instead of amplitude information. However, it may be difficult to distinguish speckle depending on the shape of speckle and the echo level. Although the speckle component may not be extracted accurately, the use of the phase information makes it possible to extract the speckle component independent of the speckle shape and the surrounding echo level.

  In this way, speckle components can be extracted using only phase information. However, speckle components can be extracted by combining amplitude information (amplitude component or its envelope component) and phase information, such as adding amplitude information to feature quantities. You may make it do. In this case, for example, in FIG. 6, phase information (azimuth direction phase change data 346a and distance direction phase change data 346b) is input to the feature value conversion unit 343. Speckle extraction may be performed by inputting to the conversion unit 343 and adding the amplitude information (pixel value) to increase the dimension of the feature amount (two dimensions in the example shown in FIG. 9).

  Thus, speckle extraction using both amplitude information and phase information enables more accurate speckle extraction.

  Next, a method for analyzing the speckle density by analyzing the speckle density from the speckle extraction result, which is the point of the present invention, will be described. The tissue property analysis using the speckle extraction result is performed in the image processing unit 30.

  FIG. 10 shows a flow of the tissue property analysis process using the speckle extraction result in the image processing unit 30.

  As shown in FIG. 10, the image processing unit 30 includes a tissue property analysis unit (analysis unit) 37 in addition to the speckle extraction unit 35 for the tissue property analysis process.

  First, amplitude data and phase data are respectively generated for the IQ data generated by the IQ detection processing of the signal processing unit 20. Amplitude data is used to generate a B-mode image, and phase data is used here for analysis of tissue properties. The phase data is input to the speckle extraction unit 35, and the amplitude data is input to the scan converter 38.

  Next, the speckle extraction unit 35 performs the speckle extraction process described above on the phase data. The speckle extraction result is input to the tissue property analysis unit 37. The tissue property analysis unit 37 analyzes the speckle density from the speckle extraction result, and analyzes the tissue property.

  On the other hand, in the scan converter 38, a B-mode image is generated from the amplitude data. At this time, the process for creating the B-mode image data from the amplitude data may be the same as the conventional image processing for generating the B-mode image, but based on the result of the analysis of the tissue property described later, the gain and dynamics Image processing parameters such as range and speckle removal processing may be changed.

  FIG. 11 shows the flow of processing in the tissue property analysis unit 37.

  First, the speckle density calculation unit 372 calculates the speckle density in the local region with respect to the speckle extraction result. The density calculation method may be a known method. For example, as shown in FIG. 12, the number of speckle pixels in the peripheral region Q (calculation range) or the real space area (volume) of the target pixel P is calculated, and the ratio to the entire region Q is output as the density. In addition, the region Q may be designated by the user or may be a region according to the tissue obtained from the edge of the amplitude information or the like, and the target pixel P is not necessarily provided. Further, since the resolution of the ultrasonic wave changes depending on the depth, a density weight corresponding to the depth may be given.

  The obtained speckle density may be output as tissue property data as it is, but may be converted into tissue information associated with the density, for example, the speed of sound, and output by the tissue property conversion unit 374. In this case, it is preferable to perform conversion based on reference data in which the relationship between the density and the tissue information is previously associated as shown in FIG. 13, for example.

  FIG. 14 shows an example of the display of the tissue property data.

  The tissue property data may be displayed as it is superimposed on the B-mode image as shown in FIG. Alternatively, the result of the tissue property analysis may be used for signal processing reconstruction or image quality adjustment.

  Hereinafter, examples of various uses of the tissue property analysis results will be described.

  For example, as described in the reference (GWMcLaughlin, “Practical Aberration Correction Methods,” Ultrasound, vol.15, no.2, 2007.pp.99-104) In the case of a device capable of generating IQ data, as shown in the sound speed analysis / correction flow in FIG. 15, the average sound speed in the screen is analyzed at the first time, and the IQ data generated at the sound speed is analyzed in the image processing unit 30. Analyze tissue properties.

  Here, as a method for analyzing the average sound speed, a method of comparing resolutions of images is used. For example, as described in the above-mentioned reference, it is preferable to use a method of performing comparison of power spectra of sound speeds by FFT (Fast Fourier Transform).

  Next, the sound speed data obtained by converting the tissue property analysis result is fed back to the signal processing unit 20, and IQ data in which the sound speed is changed again according to the value of the sound speed corresponding to the data position is generated. Since signal processing that matches the sound speed of each tissue is performed by this processing, the generated B-mode image has an image quality with resolution equal to or higher than that of the conventional method in all regions.

  In addition, when a certain average sound speed is not known as in the prior art, it is unclear how much the observed tissue differs from the sound speed assumed in the signal processing. ), The actual distance per pixel cannot be obtained, so that the required speckle density can only be determined to be relatively large or small. In this case, the speckle density for each set sound speed is required. On the other hand, when the average sound speed in the screen as in the above reference is known, the actual distance can be estimated to some extent, so the speckle density can be converted to an actual distance, so it becomes an index close to an absolute value, enabling high-precision analysis It becomes.

  Next, an example in which tissue property data is used for image quality adjustment will be described.

  As shown in FIG. 10, when the scan converter 38 converts amplitude information into a B-mode image, tissue information is given as a reference for image quality setting so that an appropriate luminance range is obtained depending on the tissue. For example, when adjusting the dynamic range in the mammary gland, as shown in the luminance distribution in FIG. 16, the dynamic range (DR) is set so that the luminance of the mammary gland and fat revealed by the tissue data becomes an output value in a certain range. ). At this time, of course, not only the dynamic range but also values such as gradation, gain, and speckle removal intensity may be adjusted according to the tissue.

  As described above, in the present embodiment, speckle noise is extracted using the phase information included in the data, and the tissue properties are analyzed from the density of the extracted speckle noise, so that optimum image processing is performed for each tissue. As a result, it is possible to accurately analyze tissue properties without being affected by variations in amplitude. Furthermore, this makes it possible to obtain a highly objective B-mode image without variations, and image diagnosis without variations can be achieved.

  Further, the ultrasonic image processing apparatus of the present embodiment is controlled by an ultrasonic image processing program stored in a memory attached to a control unit (not shown). That is, an ultrasonic image processing program is read from the memory by the control unit, and according to the ultrasonic image processing program, an ultrasonic wave is transmitted to the subject and an ultrasonic wave reflected from the subject is received. The function and the function to extract the speckle component from the phase component of the received ultrasonic signal and analyze the tissue properties contained in the ultrasonic image based on the density of the extracted speckle component are executed. The

  Note that the ultrasonic image processing program is not limited to the one stored in the memory attached to the control unit in this way, and the ultrasonic image processing program is an ultrasonic image processing apparatus such as a PC card or a CD-ROM. It may be recorded in a memory medium (removable medium) configured to be detachable and read into the apparatus via an interface corresponding to the removable medium.

  Although the ultrasonic image processing apparatus, method, and program of the present invention have been described in detail above, the present invention is not limited to the above examples, and various improvements and modifications can be made without departing from the spirit of the present invention. Of course you can go.

1 is a block diagram showing a schematic configuration of an embodiment of an ultrasonic diagnostic apparatus including an ultrasonic image processing apparatus according to the present invention. It is a graph which shows the example of constructive interference, (a) represents before interference, (b) represents after interference. It is a graph which shows the example of the destructive interference, (a) represents before interference, (b) represents after interference. It is a block diagram which shows the flow of the speckle extraction process using the phase information in an image process part. It is a block diagram which shows an example of the process in a phase change amount extraction part. It is a block diagram which shows the example of 1 structure of a speckle discrimination | determination part. It is explanatory drawing which shows the example of a speckle discriminant function. It is a block diagram which shows an example of the discriminant function production | generation process of the speckle extraction using SVM (support vector machine) in a feature-value conversion part. It is explanatory drawing which shows the example of the pixel used as a feature-value. It is a block diagram which shows the flow of the structure | tissue property analysis process using the speckle extraction result in image processing. It is a block diagram which shows the flow of the process in a structure | tissue property analysis part. It is explanatory drawing which shows the example of calculation of a speckle density. It is a table which shows the example of the reference data showing a response | compatibility with a speckle density and a substance. It is explanatory drawing which shows the example of a display of structure | tissue property data. It is a block diagram which shows the example of a sound speed analysis / correction flow. It is a diagram which shows the example of the luminance distribution by a structure | tissue.

Explanation of symbols

  DESCRIPTION OF SYMBOLS 10 ... Probe for ultrasonic waves, 20 ... Signal processing part, 30 ... Image processing part, 40 ... Display part, 32 ... Phase change amount extraction part, 34 ... Speckle discrimination | determination part, 35 ... Speckle extraction part, 37 ... Tissue property analysis unit (analysis means), 38 ... scan converter, 341 ... discriminant function creation unit, 342, 343 ... feature amount conversion unit, 372 ... speckle density calculation unit, 374 ... tissue property conversion unit

Claims (15)

An ultrasonic transmission / reception means for transmitting ultrasonic waves toward the subject and receiving ultrasonic waves reflected from the subject;
Speckle component extraction means for extracting a speckle component from the phase component using a change amount of a phase component of an ultrasonic signal received by the ultrasonic transmission / reception means;
Using the density of the extracted speckle component, analysis means for analyzing the tissue properties included in the ultrasound image;
An ultrasonic image processing apparatus comprising:   2. The ultrasonic image processing apparatus according to claim 1, further comprising display means for displaying a result of analyzing the tissue properties.   3. The ultrasonic image processing apparatus according to claim 1, wherein the speckle component extraction unit uses a change amount of at least two directions of the phase component. 4.   The ultrasonic image processing apparatus according to claim 1, wherein the speckle component extraction unit uses a discriminant function for discriminating speckles.   5. The ultrasonic image processing apparatus according to claim 1, wherein the speckle component extraction unit uses a threshold value for discriminating speckle in comparison with a change amount of the phase component. An ultrasonic image processing apparatus.   The ultrasonic image processing apparatus according to claim 4, wherein the discriminant function is set by a feature amount generated from phase data in which positions of speckles and non-speckles are known. Sonic image processing device.   The ultrasonic image processing apparatus according to any one of claims 1 to 6, wherein the density of the extracted speckle component is converted into a ratio of a speckle region in a local region. An ultrasonic image processing apparatus.   The ultrasonic image processing apparatus according to claim 1, wherein the analysis unit uses reference information in which a density of speckle components and a tissue property are associated with each other. . An ultrasonic transmission / reception means for transmitting ultrasonic waves toward the subject and receiving ultrasonic waves reflected from the subject;
Speckle component extraction means for extracting a speckle component from a combination of amplitude component or envelope component and phase component of an ultrasonic signal received by the ultrasonic transmission / reception means;
Using the density of the extracted speckle component, analysis means for analyzing the tissue properties included in the ultrasound image;
An ultrasonic image processing apparatus comprising:   10. The ultrasonic image processing apparatus according to claim 1, further comprising image processing means for adjusting the image quality of the output image using the analyzed tissue property data. Image processing device.   The ultrasonic image processing apparatus according to claim 1, wherein the tissue property output by the analysis unit is a sound velocity of the tissue.   12. The ultrasonic image processing apparatus according to claim 1, wherein the phase component has a data resolution in a direction perpendicular to a traveling direction of the ultrasonic signal, and the ultrasonic transmission / reception unit has the ultrasonic resolution. An ultrasonic image processing apparatus characterized by being at least an arrangement interval of elements for transmitting and receiving a sound wave signal.   The ultrasonic image processing apparatus according to any one of claims 1 to 12, further comprising generating images with different sound speeds from a single received data.   The ultrasonic image processing apparatus according to claim 13, further comprising: generating an image in which the sound speed is locally changed using the analyzed tissue property data. A function of transmitting ultrasonic waves toward the subject and receiving ultrasonic waves reflected from the subject;
A function of extracting a speckle component from the phase component using a change amount of the phase component of the received ultrasonic signal;
Using the density of the extracted speckle component, a function of analyzing the tissue properties included in the ultrasound image,
An ultrasonic image processing program characterized in that a computer is realized.

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