JP2006212445A - Ultrasonographic system - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To improve a diagnosis of a tissue by quantifying fineness of a speckle (speckle pattern) contained in an ultrasonic image. <P>SOLUTION: A region of interest is set on the ultrasonic image, and binarization is performed to the image in the region of interest while a threshold value is being scanned. The number of islands of high luminance is calculated for the binarized image of each threshold value, and a speckle evaluation graph is formed as its histogram. The speckle evaluation graph reflects tissue properties, and the tissue can be diagnosed by a numerical analysis of the form of the graph. Prior to the binarizing process, it is preferable to perform a process for highlighting the speckle (for processing to remove a base component). It may simultaneously display the image before its process and the image after its process. <P>COPYRIGHT: (C)2006,JPO&NCIPI

Description

  The present invention relates to an ultrasonic diagnostic apparatus, and more particularly to ultrasonic image processing.

  For example, when performing ultrasonic diagnosis of the liver, an ultrasonic probe is brought into contact with the abdomen, and ultrasonic waves are transmitted and received in that state. A B-mode image (two-dimensional tomographic image) is formed based on the received signal obtained thereby. The doctor diagnoses the presence or absence of a tumor by observing such a B-mode image.

  Speckle noise appears throughout the B-mode image. On the B-mode image, a blurry and slightly bright portion (particle or island-like region) corresponds to speckle.

  This speckle is caused by mutual interference of reflected waves (echoes) from a scatterer. In order to improve the image quality of the B-mode image, it is desired to further reduce speckle, but speckle appears to a greater or lesser extent in the B-mode image.

JP-T-5-501825

  Conventionally, it is known that there is a correlation between speckle patterns (overall patterns of speckles) on the B-mode image and tissue properties. That is, it is known that speckle patterns are not always the same, but depend on the nature of the organization. However, speckle (or its pattern) evaluation analysis has conventionally been based on visual observation by a doctor, and until now, an apparatus capable of objectively evaluating speckle or quantifying speckle has not been realized. None (however, the above Patent Document 1 describes the speckle analysis).

  The present invention has been made in view of the above-described conventional problems, and an object thereof is to positively use speckle (noise) appearing on an ultrasonic image for disease diagnosis. Another object of the present invention is to provide a speckle quantification technique. Another object of the present invention is to realize an objective analysis of speckle patterns.

(1) Preferably, by binarizing the ultrasonic image while changing the threshold level, an image forming unit that forms an ultrasonic image based on echo data obtained by transmission and reception of ultrasonic waves, Binarization processing means for generating a plurality of binarized images, area counting means for obtaining a number of independent areas having high luminance or low luminance by applying a labeling process to each of the binarized images, Graph creating means for creating a speckle evaluation graph representing the number of independent regions for each threshold level is provided.

  According to the said structure, echo data are acquired by the transmission / reception wave of the ultrasonic wave to a biological body. An ultrasonic image (desirably a B-mode image as a two-dimensional tomographic image, but may be an M-mode image, a three-dimensional image, a Doppler image, etc.) is formed based on the echo data. The ultrasound image is binarized while changing the threshold level stepwise or continuously. Normally, a threshold scan is executed, but it is also possible to perform binarization processing in parallel using a plurality of thresholds simultaneously. A labeling process for counting white or black islands (independent areas) is applied to each image after the binarization process. Various known techniques can be used for the labeling process. When the labeling process is performed on each image, the number of islands is obtained for each image, and a speckle evaluation graph is created as a histogram representing the number of islands for each threshold. This speckle evaluation graph reflects tissue properties, and can be used for diagnosis, for example, whether the tissue is normal or whether the tumor is malignant or benign. Various analysis methods can be applied to the speckle evaluation graph.

  Of course, the speckle evaluation graph may be displayed as an image, or the tissue characteristics may be automatically diagnosed from the analysis result of the speckle evaluation graph. In that case, a database or the like in which one or a plurality of graph feature amounts and diagnosis contents are associated with each other may be used.

  Desirably, it includes a region-of-interest setting unit that sets a region of interest for the ultrasonic image, and the binarization processing unit performs the binarization processing on the partial image in the region of interest to perform the plurality of operations. A binarized image is generated. According to this configuration, a property diagnosis can be performed on a specific portion (specific tissue portion) in the tissue. For example, the region of interest can be set inside the tumor in the liver, the region of interest can be set to surround the tumor, the first region of interest is set in the tumor, and the second The region of interest may be set outside the tumor, and speckle evaluation graphs created for each region of interest may be compared visually or automatically.

  Desirably, a display means for displaying the speckle evaluation graph together with the ultrasonic image is included. Further, each binarized image may be displayed.

  Preferably, it includes a graph analysis means for analyzing the speckle evaluation graph and outputting feature amount data representing the feature amount of the graph. Preferably, the feature amount data includes one or more pieces of information such as a variance value, a half width, a peak level, and an average threshold level. In particular, it is desirable to obtain a plurality of information for comprehensive evaluation.

(2) Desirably, an image forming means for forming an ultrasonic image based on echo data obtained by transmitting and receiving ultrasonic waves, and image processing of the ultrasonic image in order to diagnose tissue properties Speckle evaluation means for evaluating the appearance of speckle included in the ultrasonic image and display means for displaying the evaluation result of the speckle evaluation means are provided.

  According to the above configuration, diagnosis of a tissue can be performed using a phenomenon in which the appearance of speckles (or speckle patterns) varies depending on the properties of the tissue. In particular, since the speckle appearance can be automatically analyzed and evaluated, objective evaluation and diagnosis can be performed from the quantification.

  Preferably, the speckle evaluation means evaluates the fineness of the speckle pattern. In the evaluation of the fineness, it is desirable to apply the binarization processing based on the threshold shift described above, but the speckle fineness can be evaluated by applying various image processing techniques. Desirably, the tissue is the liver, but of course, the same technique can be applied to other organs of the human body or animals (which is preferably a real organ, but may be a fluid such as a bloodstream).

  Preferably, pre-processing means for executing pre-processing for enhancing speckles on the ultrasonic image prior to the binarization processing is included. Preferably, the preprocessing means is a filter that removes a base component superimposed on speckle. Eliminating and reducing the two-dimensional shade difference other than speckle enables more accurate quantitative speckle evaluation. That is, the evaluation accuracy can be increased.

  Preferably, the feature amount data includes a partial area of the graph. Preferably, the reference value set for the graph is used as a reference, and the area exceeding the reference value is calculated.

(3) The present invention analyzes a speckle pattern with respect to an ultrasonic image, a filter that executes preprocessing for removing a base component superimposed on speckles on the ultrasonic image, and the ultrasonic image after the preprocessing. And an ultrasonic diagnostic apparatus including the analysis means.

  Preferably, the filter is configured to calculate an average value of a plurality of pixel values in a window set on the ultrasonic image, and to subtract the average value from a pixel value of a target pixel in the window. Means for obtaining a value, and means for adding an offset value to the difference value to make it a new pixel value of the image of interest.

(4) The present invention provides an image forming means for forming an ultrasonic image based on echo data obtained by transmission / reception of ultrasonic waves, and using the ultrasonic image as an original image for speckle evaluation. Pre-processing means for executing the pre-processing, speckle evaluation means for evaluating the appearance of speckles for diagnosing tissue properties with respect to the pre-processed image, the original image and the pre-processing The present invention relates to an ultrasonic diagnostic apparatus including display means for displaying a processed image.

  According to the above configuration, the images before and after the preprocessing can be observed together. More specifically, for example, when preprocessing is performed using the above-described filter, it is advantageous in terms of appropriate speckle evaluation, but the structure that appeared in the original image is unclear. There is a possibility of becoming. That is, for example, as a result of fluctuations in the brightness of the image, the visibility of the tissue structure (for example, blood vessels) is lost, and the impression on image observation may also change. On the other hand, if the pre-processed original image is displayed together with the pre-processed image, the speckle evaluation target can be specified by displaying the pre-processed image, and the original tissue structure can be displayed as it is by displaying the original image. Can be used for diagnosis.

  Preferably, the speckle evaluation result is further displayed on the display means. In this way, an original image before preprocessing, an image after preprocessing, a speckle evaluation result, a binarized image (for example, one or a plurality of binarized images that are representative), and the like can be simultaneously used as necessary. It is desirable to display.

  Preferably, the preprocessing means includes a filter that removes a base component superimposed on speckles on the ultrasonic image. Preferably, information for identifying the original image and the pre-processed image is displayed on the display means. Such information can be expressed using letters, symbols, colors, and the like.

  Preferably, the image processing apparatus includes a region of interest setting unit that sets a region of interest for the original image, the preprocessing unit performs preprocessing on the partial image in the region of interest, and the display unit includes An original image and a partial image in the region of interest after the preprocessing are displayed. Preferably, a plurality of regions of interest can be set by the region-of-interest setting unit, and information for identifying each region of interest is displayed on the display unit. Such information can be expressed using letters, symbols, colors, and the like. In particular, different frames may be assigned to the frames of each region of interest.

  The present invention relates to an image forming unit that forms an ultrasonic image based on echo data obtained by transmitting and receiving ultrasonic waves, and a region of interest setting unit that sets a region of interest for the ultrasonic image as an original image. First synthesized image creating means for creating a first synthesized image formed by synthesizing the first marker representing the region of interest with the original image, and speckle evaluation for the partial image in the region of interest. Pre-processing means for performing pre-processing for performing, and generating a second composite image formed by combining the second marker associated with the first marker with the partial image in the region of interest after the pre-processing A second synthesized image creating means, a speckle evaluating means for evaluating the appearance of speckles for diagnosing a tissue property with respect to the partial image in the region of interest after the preprocessing, and the first The composite image and the second combination Display means for displaying an image, an ultrasonic diagnostic apparatus including a. Here, the colors of the first marker and the second marker in the correspondence relationship may be the same so that the correspondence relationship can be visually identified.

  As described above, according to the present invention, speckles or speckle patterns can be used for diagnosis.

  DESCRIPTION OF EXEMPLARY EMBODIMENTS Hereinafter, preferred embodiments of the invention will be described with reference to the drawings.

  1 to 4 conceptually show the principle of this embodiment. First, a method for quantifying the fineness of speckle (speckle pattern) will be described with reference to these drawings.

  FIG. 1 shows a sector-shaped ultrasonic image 100. This ultrasonic image 100 is a so-called B-mode image, which is formed by scanning an ultrasonic beam with an electronic sector. Of course, the present invention can also be applied to other ultrasonic images.

  A region of interest (ROI) 104 having a desired shape and a desired size is set by the user for the ultrasonic image 100. For example, if a tomographic plane of the liver is displayed as the ultrasound image 100 and a portion 102 that seems to be a tumor exists in the ultrasound image 100, a region of interest 104 is set in the region 102. Of course, this is merely an example, and a plurality of regions of interest may be set, speckle patterns may be evaluated independently for each, and the evaluation results may be compared with each other.

  FIG. 2 shows an example of an image extracted in the region of interest and an image after binarization processing using a threshold is applied to the image.

  In (A), an image in the region of interest is shown, and the above-mentioned speckle is included therein. In the present embodiment, image binarization processing is executed using each threshold value while scanning the image shown in (A) stepwise from 0 to the maximum threshold level. In FIG. 2, the binarized image when the threshold is set to 10 is shown by (B), and the binarized image when the threshold is set to 20 is shown by (C). (D) shows a binarized image when 30 is set in. For example, when the luminance of the image is in the range from 0 to 63, the threshold is scanned from 0 to 63, and a binarized image for each threshold as shown in FIG. 2 is generated. Of course, depending on how the speckle appears, the scan width of the threshold value may be limited within a certain range, or the scan range may be arbitrarily selected by the user.

  When a binarized image is generated for each threshold, in the present embodiment, the number of independent areas (islands) having high luminance, that is, pixel value 1 (or pixel value 0) is calculated. This is performed by applying a known labeling process, i.e., how many high-brightness regions are present for each binarized image.

  Then, as shown in FIG. 3, a histogram is created with the horizontal axis representing the threshold value and the vertical axis representing the number of islands. Here, each horizontal axis corresponds to a threshold value, and the above binarized image is generated for each threshold value, and the result of counting the number of islands for each binarized image is plotted on the graph as the number of labels. The Then, as indicated by reference numeral 200, a speckle evaluation graph is generated as a histogram.

  As described above, the fineness of the speckle pattern or how it appears depends on the nature of the tissue. Therefore, the shape of the speckle evaluation graph 200 represents the nature of the tissue. Therefore, if the graph itself is displayed as an image, it can be used for diagnosis of tissue properties. Further, for objective quantification, it is desirable to quantify the speckle evaluation graph 200 by some feature amount. .

  In this case, for example, various feature amount data such as a variance value, an average threshold value, a full width at half maximum, and the maximum number of labels are calculated. Preferably, the property is evaluated by combining the plurality of feature amount data.

  FIG. 4 shows the relationship between the speckle pattern (on the right side) and the speckle evaluation graph (on the left side) on a certain line. As shown in (A), the luminance difference between the peaks and valleys in the speckles varies. When the difference between them is large, the speckle evaluation graph has a gentle mountain distribution with a large dispersion value as indicated by reference numeral 200A. Note that the horizontal axis of the graphs shown on the right side of (A) and (B) is the coordinate, and the vertical axis corresponds to the luminance of the pixel.

  On the other hand, as shown in (B), if the luminance values of the peaks and valleys in the speckle are uniform to some extent, that is, if the difference between the peaks and valleys is small, a steep and small dispersion value is obtained as indicated by reference numeral 200B. Or it becomes a sharp mountain-shaped distribution.

  In any case, the speckle pattern differs depending on the nature of the tissue, and if the speckle pattern is different, the form of the speckle evaluation graph is different. Thus, speckle quantification can be realized by the form evaluation. That is, it is possible to objectively evaluate the tissue properties.

  FIG. 5 is a block diagram showing an embodiment of an ultrasonic diagnostic apparatus having such a function.

  The probe 10 is an ultrasonic probe (probe) that is inserted into a living body or used in contact with the surface of the living body. In this embodiment, the probe 10 has an array transducer including a plurality of transducer elements, and an ultrasonic beam is formed by the array transducer. Here, examples of the scanning method of the ultrasonic beam include electronic sector scanning and electronic linear scanning. In addition, when the ultrasonic beam is two-dimensionally scanned to form a three-dimensional data capturing area. The present invention can also be applied to. In this case, speckle evaluation is performed in a three-dimensional space.

  The transmitter 12 functions as a transmission beam former and supplies a transmission signal to the probe 10. The receiver 14 functions as a reception beam former, applies a phasing addition process to the reception signal output from the probe 10, and thereby outputs a reception signal after phasing addition.

  The DSC (digital scan converter) 18 has several image processing functions, but the frame memory 20 stores image data constituting a B-mode image in this embodiment. That is, for example, a logarithmic compression process or the like is performed on the reception signal (echo data) output from the receiver 14, and further a coordinate transformation is applied to form a two-dimensional tomographic image.

  In the present embodiment, the region setting unit 22 is an input unit for designating the position and size of the region of interest 104 shown in FIG.

  Image data of the entire B mode image is output from the frame memory 20, and specifically, the image data is output to the image composition unit 32. Further, the image data in the region of interest set by the region setting unit 22 is cut out from the frame memory 20 and output to the binarization processing unit 24. The binarization processing unit 24 binarizes each pixel value in the region of interest with reference to the threshold set by the threshold scan circuit 26, that is, forms a binarized image. In this case, the threshold scan circuit 26 scans the threshold from 0 to the maximum value, and a binarized image is generated for each threshold. This is shown in FIG.

  As described above, the labeling processing unit 28 is a circuit that executes a labeling process for calculating the number of white or black independent regions (islands) for each binarized image. The graph creation unit 30 is a circuit that creates a speckle evaluation graph as shown in FIG. 3 as a histogram.

  The image synthesis unit 32 is a means for synthesizing a display image, and the display image includes a two-dimensional tomographic image and a speckle evaluation graph. Of course, other images may be combined together.

  Each circuit indicated by reference numerals 36 to 48 is for quantifying the speckle evaluation graph. First, the sample number detection unit 36 is a circuit that integrates the number of labels for each threshold in the speckle evaluation graph 200 shown in FIG. 3, that is, calculates the area of the graph. The mean square value calculation unit 40 calculates the square of the area. The average threshold value calculation unit 42 is a circuit that calculates an average threshold value based on the following formula, and the variance value calculation unit 44 is a circuit that calculates a variance value based on the following formula.

  The half-value width calculation unit 46 is a circuit that calculates the half-value width of the speckle evaluation graph 200 shown in FIG. 3, and the maximum label number detection unit 48 is a circuit that detects the peak of the speckle evaluation graph 200. The graph evaluation results are output to the display unit 34, and numerical values are displayed as necessary.

  Of course, the organization itself may be automatically evaluated based on one or a plurality of graph feature amounts. In that case, diagnostic information is stored in a database or the like, and the size or value combination of each feature amount is stored. Thus, certain disease information may be displayed as an image.

  Incidentally, in the above-described embodiment, the speckle evaluation graph 200 shown in FIG. 3 is generated as a result of the labeling process for the binarized image, and the tissue property is evaluated based on the speckle evaluation graph 200. When the speckle pattern is evaluated, other image processing may be applied on the assumption that the speckle pattern is correlated with the tissue property. For example, spatial frequency analysis or predetermined filter calculation may be applied. In any case, by analyzing the speckle pattern by image processing and using it for diagnosis, it is possible to acquire information that could not be quantified on a conventional ultrasonic image and use it for disease diagnosis.

  FIG. 6 is a block diagram showing the overall configuration of an ultrasonic diagnostic apparatus according to another embodiment. In addition, the same code | symbol is attached | subjected to the structure similar to the structure shown in FIG. 5, and the description is abbreviate | omitted.

  In the configuration shown in FIG. 6, a spatial filter 50 is provided between the DSC 18 and the binarization processing unit 24. In addition, a partial area calculation unit 52 is provided after the graph creation unit 30. These will be described in detail below.

  The spatial filter 50 is a circuit that performs preprocessing for enhancing speckles. Specifically, the window 54 as shown in FIG. 7 is scanned two-dimensionally (raster scanning) with respect to the entire ultrasound image or the region of interest set in the ultrasound image, and the pixel of interest 56 at each scan position. A new pixel value is obtained. In FIG. 7, reference numeral 53 indicates an ultrasonic image, and reference numeral 56 indicates a target pixel. The target pixel 56 is set at the center of the window 54. The size of the window 54 is 11 × 11 pixels in the example shown in FIG. Of course, other sizes of the window 54 can be used.

  FIG. 8 shows a processing content in the spatial filter 50 as a flowchart. At a certain window position, an average value is first calculated in S101. Specifically, an average value is calculated for a plurality of pixel values existing in the window. In S102, the average value obtained above is subtracted from the pixel value of the target pixel, thereby obtaining a difference value. In S103, an offset value (for example, 32) is added to the difference value, and the result is used as an updated pixel value for the target pixel. In S104, it is determined whether or not there is a next window position. If there is a next window position, the window position is shifted and the same processing as described above is repeatedly executed.

  As a result, as shown in FIG. 9, preprocessing is performed on the ultrasound image. Specifically, FIG. 9A shows a state before the filtering process, and FIG. 9B shows a state after the filtering process. Although the image exists two-dimensionally, FIG. 9 shows a luminance value distribution in the X direction, that is, a luminance value graph, for explanation.

  As shown in (A), for example, when the base component 57 monotonously decreases along the X direction, if a speckle pattern is repeatedly present on the base component 57, the image is processed as it is. If this is done, it will be difficult to perform accurate speckle analysis in the labeling process. On the other hand, as shown in (B), if the process of removing the base component 57 and adding the offset value 58 is performed, the contribution due to the base component 57 is eliminated and the fluctuation component due to the speckle pattern is extracted. It becomes possible to do. That is, a speckle enhancement result can be obtained.

  The image data that has undergone the preprocessing as described above is supplied to the binarization processing unit 24 in FIG. 6, and the same binarization processing as described above is performed.

  FIG. 10 shows a specific circuit configuration example of the spatial filter 50 shown in FIG. Reference numeral 60 denotes an eleven line memory 62. These line memories 62 are connected in series with each other, and data is output pixel by pixel from the entire input and output and between each line 62. These outputs are added by the adder 64, and the added value is temporarily stored in the latch 65 and then output. The output value is supplied to the A input terminal of the arithmetic unit 70 and also input to the delay line 66 for timing adjustment. Here, the delay line 66 is composed of 11 latches 67, and the added value is delayed by 11 steps. Then, the output of the delay line 66 is input to the B input terminal of the computing unit 70. The arithmetic unit 70 is a circuit that subtracts the value input to the B input terminal from the value input to the A input terminal. The subtraction result is input to the adder 71, and the adder 71 is provided in the subsequent stage. The outputs from the latch 72 are added, and the addition result is input to the latch 72.

  Therefore, with this configuration, all pixel values for a window of 11 × 11 pixels are added, which is obtained at each window position. Then, in the averaging circuit 73, the added value is divided by the total number of pixels, and as a result, an average value is obtained. The average value is input to the B input terminal of the computing unit 74. On the other hand, the pixel value of the target pixel is output from the central line memory, and the pixel value is input to the delay line 68. This delay line 68 is a circuit for timing adjustment, and is composed of six latches 69. The output of the delay line 68 is supplied to the A input terminal of the arithmetic unit 74.

  The computing unit 74 subtracts the value input to the B input terminal from the value input to the A input terminal, and outputs the subtraction result to the adder 75. That is, the average value is subtracted from the target pixel, and the difference value obtained thereby is added to the offset value in the adder 75. The added value is output via a latch 76.

  Of course, the configuration shown in FIG. 10 is an example, and other circuit configurations may be adopted as the spatial filter. Further, spatial filtering may be realized by software regardless of the hardware configuration. The same applies to other circuit configurations.

  As described above, the partial area calculation unit 52 is provided in the configuration shown in FIG. This will be described with reference to FIG.

  FIG. 11 shows a speckle evaluation graph 200 obtained by speckle analysis. A partial area 82 is calculated based on a reference value 80 obtained by user setting or automatically for the speckle evaluation graph 200. Specifically, the area of the portion exceeding the reference value 80 is calculated as the partial area 82. This partial area 82 means the total number of labels corresponding to each threshold value exceeding the reference value 80, and indicates one feature amount of the speckle evaluation graph 200. Accordingly, such a partial area 82 is output to the display unit 34 and numerically displayed as necessary, or expressed by coloring or the like on the graph. Of course, as described above, the partial area may be used even when a speckle pattern is evaluated by combining a plurality of feature amounts.

  In the above-described embodiment, the speckle is emphasized by positively removing the two-dimensional shading difference unrelated to the speckle existing on the image, and as a result, the speckle pattern is analyzed with high accuracy. Is possible. In addition, a partial area can be used in the analysis, and it is possible to realize another side analysis of the speckle pattern. The spatial filter 50 described above can also be used in image processing of an ultrasonic image other than the analysis of the speckle pattern.

  FIG. 12 is a block diagram showing the configuration of an ultrasonic diagnostic apparatus according to still another embodiment.

  In FIG. 12, the same components as those shown in FIGS. 5 and 6 are denoted by the same reference numerals, and the description thereof is omitted. In FIG. 6, one line is drawn from the DSC 18, and the line represents the data of the entire ultrasound image and the data of the partial image in the region of interest, but in FIG. 12, to help technical understanding. The two data are represented by two lines. That is, the entire ultrasonic image data 300 as the original image output from the DSC 18 is output to the image synthesis unit 306, and the data 300 is output to the spatial filter 50 as necessary. The partial image data 302 in the region of interest output from the DSC 18 is output to the spatial filter 50 that executes preprocessing.

  On the other hand, the coordinate data for one or a plurality of regions of interest set by the region setting unit 22 is output to the DSC 18 and also to the image composition unit 306. Further, the image data 304 after the preprocessing by the spatial filter 50 is output to the binarization processing unit 24 and simultaneously output to the image composition unit 306.

  As described above, in addition to the graph data created by the graph creation unit 30, the original image data 300 and the pre-processed partial image data 304 in the region of interest are input to the image composition unit 306. . The image synthesizing unit 306 has an image synthesizing function, and particularly has a function of synthesizing an ultrasonic image and a graphic image such as a marker. A specific image processing function of the image composition unit 306 will be described with reference to display examples shown in FIGS.

  FIG. 13 shows a display example on the display unit 34. On the display screen 308, the ultrasonic image before the pre-processing by the spatial filter 50, that is, the original image 310 is displayed, and the pre-processed image 312 after the pre-processing by the spatial filter 50 is displayed along with the ultrasonic image. Is done. When such a display is performed, the entire ultrasonic image data 300 is input to the spatial filter 50. In other words, the speckle pattern evaluation target is the entire image.

  In order to visually recognize the relationship between the original image 310 and the preprocessed image 312, an arrow mark 314 is displayed between the images 310 and 312 on the display screen 308. By observing the direction of the arrow mark 314, the images 310 and 312 before and after the preprocessing can be confirmed. Of course, such image identification information is not limited to the arrow mark 314 but may be a character or another symbol. On the display screen 308, for example, a speckle evaluation graph is displayed as the speckle pattern evaluation result, and the analysis result of the graph is displayed as necessary.

  According to this display example, in particular, since the original image is displayed together, the tissue structure can be clearly recognized, and the preprocessed image can be observed in comparison with the original image. In addition, a comprehensive diagnosis of the tissue can be performed.

  As described above, the image composition unit 306 generates a display image including a plurality of images and graphs. If necessary, one or a plurality of binarized images may be displayed on the display screen.

  In another display example shown in FIG. 14, an original image display area 400 and a pre-processed image display area 402 are set on the display screen 320. An original image 322 is displayed in the original image display area 400. On the original image, one or a plurality of regions of interest can be set by a user operation. In the example shown in FIG. 14, two regions of interest 324 and 326 are set. In order to visually identify them, in the present embodiment, rectangular markers (first markers) 324A and 326A that identify the two regions of interest 324 and 326 are combined and displayed. These correspond to the frame of each region of interest. The colors of the markers 324A and 326A are different from each other, the color of the marker 324A is, for example, red, and the color of the marker 326A is, for example, blue. Of course, each region of interest may be distinguished by a technique other than coloring. For example, methods such as line type change, luminance change, and insertion of characters and symbols can be used.

  In the pre-processed image display area 402, two partial images 328 and 330 after the pre-process corresponding to the two regions of interest are displayed in the illustrated example. Here, rectangular markers (second markers) 328A and 330A are combined and displayed for each of the partial images 328 and 330, and the former color is, for example, red, and the latter color is, for example, blue. That is, the correspondence between the region of interest and the partial image becomes clear at a glance by applying the same coloring to each marker. In this case, as described above, the correspondence between each region of interest and each pre-processed partial image may be displayed using a method other than coloring.

  The image composition unit 306 has a function of composing the image and the marker as described above, and a function of configuring a display screen including each image. Of course, a speckle evaluation graph, a graph analysis result, a binarized image, and the like are displayed on the display screen 320 as necessary.

  As described above, according to the display example shown in FIG. 14, the position and size of the region of interest can be confirmed on the original image, and the structure of the tissue can be clearly understood on the original image. The pre-processed partial image that is the object of evaluation can be confirmed. Then, by considering the speckle evaluation graph and the analysis result together, a comprehensive tissue diagnosis can be performed.

  In addition, when the user sets each region of interest, for each region of interest, an initial marker of a certain size indicating the region of interest is automatically displayed on the original image, and it is moved manually or its size Is changed manually to set a region of interest having a desired size at a desired position. In that case, the color of the marker may be designated by the user for each region of interest, or the color of each marker may be automatically determined. The display unit 34 is actually configured by a display processing unit and a display unit, but the display unit may be configured by a single display or a plurality of displays (for example, a main display and an auxiliary display). It may be configured.

It is a figure which shows the relationship between an ultrasonic image and a region of interest. It is a figure which shows the image in a region of interest, and the binarized image after binarizing it. It is a figure which shows an example of a speckle evaluation graph. It is a figure which shows the relationship between a speckle evaluation graph and a speckle pattern. 1 is a block diagram showing an overall configuration of an ultrasonic diagnostic apparatus according to the present embodiment. It is a block diagram which shows the whole structure of the ultrasound diagnosing device which concerns on other embodiment. It is a figure for demonstrating the window which a spatial filter has. It is a flowchart for demonstrating the processing content in a spatial filter. It is a figure for demonstrating the removal of a base component, and offset addition. It is a figure for demonstrating the specific structural example of a spatial filter. It is a figure for demonstrating the area calculation with respect to a graph. It is a block diagram which shows the whole structure of the ultrasonic diagnosing device which concerns on other embodiment. It is a figure which shows the example of a display of an image. It is a figure which shows the other example of a display of an image.

Explanation of symbols

  10 probes, 12 transmitters, 14 receivers, 18 digital scan converters (DSC), 20 frame memories, 22 area setting units, 24 binarization processing units, 26 threshold scanning circuits, 28 labeling processing units, 30 graph creation Unit, 32 image composition unit, 34 display unit, 36 sample number detection unit, 40 mean square value calculation unit, 42 average threshold value calculation unit, 44 variance value calculation unit, 46 half value width calculation unit, 48 maximum label number detection unit, 50 Spatial filter, 52 Partial area calculator.

Claims (9)

A filter that performs a pre-processing for removing a base component superimposed on speckle on an ultrasonic image;
Analyzing means for analyzing a speckle pattern for the ultrasonic image after the preprocessing;
An ultrasonic diagnostic apparatus comprising: The apparatus of claim 1.
The filter is
Means for calculating an average value of a plurality of pixel values in a window set on the ultrasonic image;
Means for subtracting the average value from the pixel value of the pixel of interest in the window to obtain a difference value;
Means for adding an offset value to the difference value and setting it as a new pixel value of the image of interest;
An ultrasonic diagnostic apparatus comprising: Image forming means for forming an ultrasonic image based on echo data obtained by transmission and reception of ultrasonic waves;
Preprocessing means for performing preprocessing for performing speckle evaluation on the ultrasonic image as an original image;
Speckle evaluation means for evaluating the appearance of speckles for diagnosing tissue properties with respect to the image after the preprocessing;
Display means for displaying the original image and the pre-processed image;
An ultrasonic diagnostic apparatus comprising: The apparatus of claim 3.
The ultrasonic diagnostic apparatus, wherein the display means further displays the speckle evaluation result. The apparatus of claim 3.
The ultrasonic diagnostic apparatus, wherein the preprocessing means includes a filter that removes a base component superimposed on speckles on the ultrasonic image. The apparatus of claim 3.
Information for identifying the original image and the pre-processed image is displayed on the display means. The apparatus of claim 3.
A region of interest setting means for setting a region of interest for the original image;
The preprocessing means performs preprocessing on a partial image in the region of interest;
The ultrasonic diagnostic apparatus according to claim 1, wherein the display means displays the original image and a partial image in the region of interest after the preprocessing. The apparatus of claim 7.
A plurality of regions of interest can be set by the region of interest setting means,
The ultrasonic diagnostic apparatus according to claim 1, wherein information for identifying each region of interest is displayed on the display unit. Image forming means for forming an ultrasonic image based on echo data obtained by transmission and reception of ultrasonic waves;
Region of interest setting means for setting the region of interest for the ultrasound image as an original image;
First synthesized image creating means for creating a first synthesized image obtained by synthesizing a first marker representing the region of interest with the original image;
Preprocessing means for performing preprocessing for performing speckle evaluation on the partial image in the region of interest;
Second synthesized image creating means for creating a second synthesized image obtained by synthesizing the second marker associated with the first marker with the partial image in the region of interest after the preprocessing;
Speckle evaluation means for evaluating the appearance of speckles for diagnosing the tissue properties for the partial image in the region of interest after the preprocessing;
Display means for displaying the first composite image and the second composite image;
An ultrasonic diagnostic apparatus comprising:

Images