JP2003334194A - Image processing equipment and ultrasonic diagnostic equipment - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide image processing equipment and ultrasonic diagnostic equipment which extract the contours of an object, corresponding to a local defect or indistinctness in a tomographic image or the like. <P>SOLUTION: An area dividing part 103 divides an ultrasonic tomographic image into small areas on the basis of initial contours. An evaluation value calculating part 104 calculates an evaluation value from the information on a luminance value (contrast distribution or the like), the information on a position (distance from a reference point or the like), the information on a shape (existence or nonexistence of edge or the like), etc., in each small area, for instance. An area selecting part 106 selects the small area in conformity with the calculated evaluation value. A part 105 for processing by area executes an image processing corresponding to the selected small area. An image reconstructing part 107 reconstructs the tomographic image by using the small area subjected to the image processing. <P>COPYRIGHT: (C)2004,JPO

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an ultrasonic diagnostic apparatus for generating a tomographic image used in clinical medicine or the like, or an image processing apparatus for processing an image displayed on various video equipment, a mobile phone, etc., In particular, it relates to image quality improvement techniques such as contour extraction for those images.

[0002]

2. Description of the Related Art Conventionally, image processing may be performed in an ultrasonic diagnostic apparatus, various video equipment, etc. in order to extract a contour of a specific object (a soft tissue or a face of a living body) in an image. .

An ultrasonic diagnostic apparatus is capable of non-invasively obtaining a two-dimensional tomographic image of an object. For example, in the field of clinical medicine, it is highly safe for living organisms and is therefore widely used as an indispensable apparatus. It is popular. Moreover, the same can be said for a device to which ultrasonic waves are applied in other fields.

Generally, an ultrasonic diagnostic apparatus receives an echo obtained by reflecting a part of ultrasonic waves transmitted from an ultrasonic probe at a structural change point (changed surface) of tissue in an object, A tomographic image of the object is generated based on this echo. This reflected wave (ultrasonic echo)
Is weaker than the transmitted ultrasonic waves, and therefore an amplification process (gain process) is performed when a luminance signal for displaying an image is generated. Amplification (gain) at this time
Conventionally, the STC (Sensit
This is performed by operating a plurality of sliders (for example, 16 sliders), which are classified by depth level of the object, called “activity time control” (however,
Processing is also performed in part by a logarithmic amplifier).

As described above, the amplification processing in the conventional ultrasonic apparatus is intended to adjust the image quality by manually controlling the contrast and dynamic range of the tomographic image.

On the other hand, based on the tomographic image, the fetus, internal organs,
By quantitatively calculating the values of the area, volume, change amount, etc. of the circulatory organ, it is possible to improve the quality of screening and precision examination in the ultrasonic diagnostic apparatus. In this case, the method of extracting contours and boundaries for calculating the area and volume of the organ or the like to be diagnosed is very important.

However, manually controlling the contrast and the like including STC is complicated and requires skill. Furthermore, when extracting the contours of an organ of interest or the like only by artificial tracing, it is necessary to always accurately trace the contours using a pointing device or the like, which requires a great deal of labor. In light of this background, so far, automatic image correction methods performed on tomographic images,
Several contour / boundary extraction methods have been proposed (for example,
See Patent Document 1).

In the "image quality automatic adjustment method" disclosed in the above-mentioned Patent Document 1, the characteristics of the tomographic image (the luminance signal distribution of the tomographic image often shows a steep Gaussian distribution, and the effective dynamic range is (Narrowness etc.)
It provides a way to automatically control the gain. This is to uniformly measure the distribution of luminance values for the entire image and perform gain control.

Another feature of the tomographic image is that a part of the image is missing or a locally unclear portion frequently appears. However, in the method of uniformly processing the entire image as described above, In some cases, it is not possible to sufficiently improve the image quality of a tomographic image having such a defect or unclearness.

A similar problem also occurs in the contour and boundary extraction method. The contour and boundary extraction methods that have been proposed so far assume that the contour is clearly drawn in the tomographic image. This applies not only to the automatic contour / boundary extraction method, but also to a so-called semi-automatic extraction method in which tracing is performed after manually providing an initial contour (see, for example, Patent Document 2). In the "ultrasonic image diagnostic apparatus" disclosed in Patent Document 2, first, the outline of the tissue of interest is roughly manually traced with a mouse or the like to extract a reference outline or the like. Then, the starting point for extracting the contour and the like is set. In this case, the scan lines extend radially from the start point, and the detection range is set around the intersection point with the outline or the like manually on the line. Further, the image data of the tomographic image within this detection range is binarized with a threshold to detect the position of the contour or the like to be corrected, and when the position of the contour or the like is detected, it is manually set. Correct boundaries are obtained by correcting boundaries such as contours.

On the other hand, recently, various video equipment (especially,
An image of a person or a face (hereinafter referred to as a “person image”) is often generated by using a photographing function of a mobile phone, a PDA, or the like. At this time, the contour of the face or the like is extracted. In some cases, it may be desirable to perform image processing that better suits the photographer's wishes. Specifically, in a human image, the outline of a person (especially a face) may become unclear due to phenomena such as the background and steam at the time of shooting, but in this case, the outline of the person does not appear unnatural. Image processing is performed to clarify the above.

27 and 28 are views showing two examples in the case where the contour extraction is performed in the conventional video equipment. 27A to 27C are diagrams showing a successful example of contour extraction performed on a human image in a conventional video device.

FIG. 27A shows an original image taken by a mobile phone. As shown in FIG. 27 (a),
In this original image, only the face of a person is photographed. Consider a case where contour extraction is performed on this original image (see, for example, Patent Document 3). According to the method of extracting contours in two steps disclosed in Patent Document 3, first (see FIG.
Initial contours are set (as shown in FIG. 7 (b)) and then finer contours are extracted (as shown in FIG. 27 (c)).

However, if a part of the original image interferes with the contour extraction, the expected contour extraction may not be possible. 28A to 28C are diagrams showing an example of failure in contour extraction performed on a human image in a conventional video device. FIG. 28 (a) is an original image similar to that of FIG. 27 (a), but since there is a portion (for example, a portion where water vapor is photographed) that obstructs the contour extraction in the lower right corner of the screen, The difference is that the outline is partially blurred.

[0015]

[Patent Document 1] Japanese Patent Laid-Open No. 2002-209891

[0016]

[Patent Document 2] JP-A-11-164834

[0017]

[Patent Document 3] Japanese Patent Laid-Open No. 2002-224116

[0018]

However, if the operator is skilled in the operation, it can be carried out more quickly than the extraction by using only the manual trace, but it is not completely automated. In addition, this method does not provide a calibration method when the extracted contour or the like is incorrect. Naturally, the contour extraction result depends on the setting of the threshold value, which is a prerequisite for binarization, and there is no way to make it for the area where the contour is not clearly drawn originally.

As described above, if the tomographic image has a partial defect or a locally unclear portion, the conventional image adjustment method, contour extraction method, or the like fails locally (in some cases, completely). However, there is a problem that it may not work.

Further, when contour extraction is performed on the original image of FIG. 28 (a) by applying the same method as in FIG. 27 (FIG. 28 (b) shows a case where an initial contour is set). .), As shown in FIG. 28C, the result extracted as a fine contour is different from the actual contour. As described above, in the case where a captured original image generally has a portion that hinders contour extraction, there is also a problem that the expected contour extraction cannot be performed.

Therefore, the present invention has been made in view of the above-mentioned problems, and provides various image processing methods that can be applied in consideration of the local properties of an ultrasonic image, and further, by the methods, the image processing method can be used. The purpose is to provide automatic correction and contour extraction method.

[0022]

In order to achieve the above object, an image processing apparatus according to the present invention includes an image acquisition unit for acquiring image data, and an image represented by the acquired image data. Area dividing means for dividing the area into areas, area selecting means for selecting one or a plurality of the small areas, and area-specific processing means for performing image processing on each of the selected small areas are provided.

In order to achieve the above object, the ultrasonic diagnostic apparatus according to the present invention is an ultrasonic diagnostic apparatus for displaying a tomographic image of a subject generated based on reflection of ultrasonic waves. Image acquisition means for acquiring data, area division means for dividing the tomographic image represented by the acquired image data into a plurality of small areas, area selection means for selecting one or a plurality of the small areas, and the selection Area-specific processing means that performs specific image processing for each of the small areas that have been processed, and display means that displays the image of the small area that has undergone the image processing.

In order to achieve the above object, the present invention can be realized as a program including all the steps as the characteristic means of the image processing apparatus and the ultrasonic diagnostic apparatus. The program can be stored not only in the ROM or the like included in the image processing apparatus or the ultrasonic diagnostic apparatus, but also in the distribution via a recording medium such as a CD-ROM or a transmission medium such as a communication network.

[0025]

BEST MODE FOR CARRYING OUT THE INVENTION Embodiments of the present invention will be described below with reference to the drawings.

(Embodiment 1) FIG. 1 shows an ultrasonic diagnostic apparatus 10 which is one of image processing apparatuses in the present embodiment.
3 is a block diagram showing an outline of a functional configuration of FIG. This device 10
Has a function of improving the image quality according to the situation of the portion even if the tomographic image is locally unclear. The ultrasonic probe 11, the transmission / reception unit 12, the pulsation detection unit 13, and the operation are performed. Part 1
4, an image processing unit 15 and a data output unit 16.

The ultrasonic probe 11 is generally called a probe and is, for example, a phased array type electronic scanning type probe. The ultrasonic probe 11 emits ultrasonic waves (for example, ultrasonic pulses) based on the control signal received from the transmitting / receiving unit 12. Further, the ultrasonic probe 11 converts an ultrasonic wave (hereinafter, referred to as “ultrasonic echo”) reflected from the living body of the subject into an electric signal and transmits the electric signal to the transmitting / receiving unit 12.

The transmission / reception unit 12 includes, for example, a CPU and a ROM.
And a RAM and the like, and has a function of controlling the entire apparatus 10 and transmitting / receiving ultrasonic waves. Further, the transmitting / receiving unit 12 is a transmission beamformer for generating ultrasonic waves from the ultrasonic probe 11 and a reception beamformer for receiving electric signals transmitted from the ultrasonic probe 11 that has detected ultrasonic echoes. Ultrasonic probe 1
The electric signal received from 1 is amplified and transmitted to the image processing unit 15. Further, the transmission / reception unit 12 is the operation unit 1
An instruction from an operator is accepted via 4.

The pulsation detection unit 13 is, for example, a pulsation sensor, converts the detected pulsation of the subject into an electric signal, and transmits the electric signal to the image processing unit 15. The operation unit 14 includes switches, a touch panel, and the like, receives operations performed by an operator on these, and sends and receives control signals corresponding thereto to the transmission / reception unit 12.
Or to the image processing unit 15.

The image processing section 15 generates image data of a tomographic image based on the electric signal received from the transmitting / receiving section 12. Further, the image processing unit 15 divides the generated tomographic image into small areas, and performs image processing for each small area. Further, the image processing unit 15 reconstructs a tomographic image based on the image data after the image processing, and the image output unit 16 outputs the image data.
Send to.

The data output unit 16 has a function of receiving image data of a tomographic image (for example, a B-mode tomographic image) formed by the image processing unit 15 and displaying the image data on a liquid crystal display which is an observation monitor. , A graphic accelerator and a scan converter.

FIG. 2 shows the image processing unit 15 in FIG.
3 is a block diagram showing the detailed functional configuration of FIG. The image processing unit 15 includes an image generation unit 110, a contour extraction unit 111, a control unit 112, an image memory 101, a general-purpose memory 102, and a calculation unit 109. The arithmetic unit 109 is a characteristic part of the present invention, and is realized by hardware such as a dedicated processor or software. The calculation unit 109 includes an area division unit 103 and an evaluation value calculation unit 10.
4, a region-specific processing unit 105, a region selection unit 106, and an image reconstruction unit 107.

The image generator 110 generates image data by A / D converting the electric signal received from the transmitter / receiver 12. Further, the image generation unit 110 transmits the generated image data to the control unit 112.

Here, the image data means the ultrasonic probe 1.
It is two-dimensional luminance data or the like generated for each scanning by 1 and is data for displaying in the B mode or the like.
The contour extraction unit 111 extracts the contour of an object such as the left ventricle of the heart based on the image data stored in the image memory 101 to generate contour data. For the method of extracting the contour based on the image data, see JP-A-2002-22.
The details are described in Japanese Patent No. 4116. The outline of this method is to extract a rough initial contour by using the "binarization" and "degeneration" methods for a tomographic image of an object, and to perform a dynamic contour model (SNA) on the initial contour.
KES) is applied to finally obtain a fine contour while performing a convergence calculation. Here, the contour data refers to data of the coordinates (X coordinate and Y coordinate) of a plurality of pixels forming the contour line of the object extracted based on the image data for one frame.

The control unit 112 is, for example, a ROM or a RAM.
And the like, and mainly instructs each unit in the image processing unit 15 to execute each process and controls the timing of these processes.

The image memory 101 is, for example, a RAM, and is subjected to image processing of the image data of the tomographic image generated by the image generation unit 110 and image processing by the region-based processing unit 105 described later according to an instruction from the control unit 112. Store image data.

The general-purpose memory 102 is, for example, a RAM, and data (for example, data stored in the image memory 101) other than image data (that is, data stored in the image memory 101) relating to the tomographic image generated by the image generation unit 110 according to an instruction from the control unit 112. , Data relating to region division, data associated with contours, data relating to evaluation value calculation, data relating to image processing, etc.).

The area dividing section 103 divides the tomographic image generated by the image generating section 110 into a plurality of areas. As a specific example of the dividing method in this case, the initial contour of the object is automatically extracted or specified by an operator's operation, and the initial contour is radially divided from the center of gravity of the initial contour. A method of generating a boundary line having a width of a certain number of pixels outside and radially dividing a donut-shaped region sandwiched by the initial contour and the boundary line at a predetermined angle (eg, π / 4) The vertical axis and the horizontal axis of each are divided into N equal parts (for example,
There is a method of dividing into four equal parts.

FIG. 3 is a diagram for explaining an example of the above division method. In FIG. 3, the rectangular outer frame 200 is the outer edge of the displayable area on the observation monitor of the data output unit 16, and the area surrounded by the fan-shaped thick line 201 is the substantial display area of the tomographic image. In FIG. 3, eight divided small areas 310 to 380 are shown.

In the following, the small area 310 is referred to as the area dividing unit 10.
The procedure until it is specified by 3 will be described. First, the initial contour 210 is automatically specified or specified by an operator, and the center of gravity G211 is calculated. Further, a vertex T212 as a reference point on the initial contour 210
(That is, the point of the initial contour 210 having the largest Y coordinate value) is specified, and the straight line connecting the center of gravity G211 and the vertex T212 is extended to intersect the thick line 201 at points P213 and C.
Ask for 214.

Next, two straight lines 202 and 203 having angles of (π / 2) and (−π / 4) are obtained with respect to the straight line PC connecting the point P213 and the point C214, and each of them is the above-mentioned. The points that intersect the initial contour 210 are point I21.
5 and a point E217, and points intersecting with the thick line 201 are a point R216 and a point Q218, respectively.

The point I 215 and the point R specified as described above
A closed area (area hatched in FIG. 3) constituted by 216, a point Q218 and a point E217 represents a small area 310 which is one of the divided areas. Similarly, the small areas 320 to 380 can be specified.

FIG. 4 is a diagram for explaining a modification of the above division method. In the case of the division method shown in FIG. 3 above, the area sandwiched between the initial contour 210 and the thick line 201 is the target of division (limited to the substantial display area), but in the case of FIG. It is expanded to a rectangular outer frame 200. Therefore, the point I215 and the point RR2 are divided by the division.
A closed area (area indicated by hatching in FIG. 4) constituted by 19, the point V221, the point QQ220, and the point E217 is the identified small area 410 in this case.

FIG. 5 is a diagram for explaining an example of the above division method. In the case of the division method shown in FIG. 3, the area between the initial contour 210 and the thick line 201 is targeted for division, but in FIG. 5, a fixed pixel (for example, 50 pixels) is located outside the initial contour 210. An example is shown in which a boundary line 501 is provided at a distant position, and a donut-shaped region sandwiched between the initial contour 210 and the boundary line 501 is divided into eight, as in the above case. Therefore, in this case, the closed area (the area shaded in FIG. 5) formed by the point I215, the point J502, the point F503, and the point E217 is
It is the small area 510 specified by this division method.

FIG. 6 is a diagram for explaining a modification of the above division method. In the case of the dividing method shown in FIG. 5, the donut-shaped area sandwiched between the initial contour 210 and the boundary line 501 is the target of division, but in FIG. For example, a boundary line 601 is provided at a position apart by 12 pixels), and the donut-shaped region sandwiched between the boundary line 601 and the boundary line 501 is divided into eight, similar to the above case. So in this case,
The closed area (the area shaded in FIG. 6) constituted by the point H602, the point J502, the point F503, and the point D603 is the small area 61 specified by this division method.
It is 0.

FIG. 7 is a diagram for explaining an example of the above division method. In the case of the division method of or, the method of radially dividing the center of gravity G211 of the initial contour 210 is used as a reference, but in the division method of FIG. 7, the X axis and the Y axis in the displayable area of the observation monitor are displayed. An example is shown in which the length is divided into four equal parts to generate a small area. Therefore, in this case, the rectangular outer frame 200 which is the displayable area is divided into 16 small areas equivalent to the rectangular small area 710 having a pixels in the X direction and b pixels in the Y direction. . Note that here, as the division example of the area division unit 103, the division examples as shown in FIGS. 3 to 7 are shown, but the method of dividing the area is not limited to these, and any existing method can be used. The division may be performed by the division method (for example, a method of dividing by π / 3 counterclockwise using the straight line connecting the center of gravity G211 and the point T212 in FIG. 3 as a reference line).

The evaluation value calculation unit 104 includes the area division unit 103.
An evaluation value for quantitatively grasping the quality and characteristics of the tomographic image is calculated for each of the small regions divided by. The following methods are available for calculating the evaluation value.

(1) Method of Using Luminance Value in Small Area This is a method of calculating an evaluation value based on the average or variance of the brightness values of the pixels forming the image of the small area.

(2) Method of using information on shape This is the circularity φ calculated from the shape of the contour of the object in the small area (φ = φ where the contour line length is L and the cross-sectional area is A).
It is represented by 4πA / L ** 2, and is 1.0 for a circle. This circularity becomes smaller as the shape becomes more complicated. ) And sharpness are used as evaluation values. In addition to these, for example, the evaluation value is obtained by using position-related data such as the distance between the barycentric position of the contour of the specified object (that is, the reference point of the entire tomographic image) and the reference point of each small area. There is also a method. As a specific example of the method of using the data regarding the position as the evaluation value, it will be explained with reference to FIG. (In this case, the reference point of each small area is taken as the center of gravity of each.) As an evaluation value, there are cases where four of the smaller values are selected.

(3) Method using edge information In this method, an arbitrary edge detection filter (two-dimensional differentiation using a filter window) is applied to a small area, and the output result is evaluated value (for example, x direction or The differential amount in the y direction, the edge strength, etc.).

(4) Method of using binarized information In this method, the brightness value in the small area is binarized using a predetermined threshold value or a threshold value dynamically determined from the brightness value distribution in the small area, Distribution and shape of binarized data (for example,
It is a method that uses statistical or shape geographical data such as kurtosis) as the evaluation value.

(5) Method of using the degree of separation in luminance value The degree of separation in luminance value means that when the luminance values are classified into "0" and "1" classes, the variation between classes is the variation of all luminance values. It refers to the ratio to. When the luminance value is completely separated into “0” and “1”, the value of the degree of separation is 1.0 (maximum value). Regarding the method of using this degree of separation, Kazuhiro Fukui, "Object Contour Extraction Based on Degree of Separation Between Regions" (Society theory D-II vol. J80-D-II, n
o. 6, pp. 1406-1414, June 199
Details are described in 7).

(6) Method of using maximum brightness value or minimum brightness value For example, the maximum brightness difference = maximum brightness value−minimum brightness value,
There is a method of using this maximum brightness difference as an evaluation value.

The above "(1) Method of using brightness value in small area" will be described below. For example, in the case of the method using the luminance value, the evaluation value may be simply “the luminance distribution in the small area”, or the average value of the luminance values may be used as the center.
The “width of the luminance value range that occupies 80% of the entire luminance value histogram” may be used as the evaluation value.

Regarding the latter method, referring to FIG.
This will be described more specifically. As shown in FIG. 8, the brightness value distribution in a certain small area is 0 to 255, and the brightness average value is “12”.
Consider the case of "0". In this case, the luminance value in the small area is sampled, and 80% of the total pixels (total pixel number is 1
In the case of a small area consisting of 000, “α” is calculated when the luminance value of 800 pixels) satisfies “120 ± α (α: natural number)”, and the evaluation value at this time is set to “2α”. Here, as the evaluation value calculation example of the evaluation value calculation unit 104, the calculation examples as shown in the above (1) to (6) have been described, but the evaluation value calculation method in the evaluation value calculation unit 104 is However, the evaluation value may be calculated based on any existing mathematical expression or image processing.

The area-specific processing section 105 is an area dividing section 103.
Image processing is performed for each small region divided by. The image processing in this case mainly refers to processing for improving the image quality of the tomographic image in each small area.
Processing for facilitating evaluation in (for example, normalization processing for suppressing variation in evaluation value between small areas) and when an image is reconstructed by an image reconstructing unit 107 described later, It may be a process for improving the performance of the connected device, stabilizing the operation, improving the image quality, or the like.

The processing for improving the image quality includes, for example, binarization processing, contrast conversion processing, bias conversion processing, noise removal processing, morphology processing, edge extraction processing, and edge enhancement processing. The treatments may be combined.

An outline of each of the above processes will be described with reference to FIG. FIG. 9A is a diagram showing a state of input luminance and output luminance in the binarization processing. FIG. 9 (a)
As shown in, when the threshold value of the input brightness value is "128", when the input brightness value is 128 or more, the output brightness value changes from 0 to 255.

FIG. 9B is a diagram showing the relationship between the input luminance value and the output luminance value in the contrast conversion processing and the bias conversion processing. A curve 901 in FIG. 9B is one that nonlinearly relates the input luminance value and the output luminance value by the contrast conversion processing. In addition, the curve 9 in FIG.
Reference numeral 02 denotes a bias conversion process for adding (biasing) a constant brightness value to the input brightness value and outputting the output brightness value. In this case, the brightness value to be biased is “6
It is 0 ". For reference, a curve 903 of input luminance value = output luminance value is shown in FIG. 9B.

As the noise removing process, for example, there is a two-dimensional low pass filter or the like. Morphological processing is a type of non-linear filtering processing,
It refers to a filtering process based on an operation such as “shift overlay (dilation)” or “scratch out (erosion)” performed for the purpose of feature extraction from a value image or a grayscale image. Details of this morphological treatment are described in "Morphology" by Hidefumi Obata (Corona Publishing Co.).

The edge extraction process is a process of extracting an edge representing a boundary between regions (for example, an object and a background) in an image, and there is a method using a primary differential filter or a secondary differential filter.

The edge enhancement process is a process for further increasing the difference in gray level between the edge and the other portion in the tomographic image, and there is a method of converting the brightness value dispersion. FIG. 10 is a diagram showing an example of a method for converting the luminance value variance. In the example of FIG. 10, the average value of the brightness values (for example,
The curve 1001 in which the dispersion of the brightness values is concentrated around 120) is converted into a curve 1002 so as to reduce the dispersion of the brightness values.

The area selecting unit 106 specifies an arbitrary number of small areas from the small areas divided by the area dividing unit 103. In this case, as the specifying method, the small areas calculated by the evaluation value calculating unit 104 are ranked in descending order from the highest evaluation value.
Alternatively, a predetermined number may be selected, or small areas having a low evaluation value may be selected in order (ascending order). For example, in the case of the evaluation value “2α” based on the above-mentioned luminance value, by selecting in descending order of “2α”, the small regions with clear contrast (that is, wide contrast range) are selected in order. can do. On the contrary, by selecting in order from the smallest “2α”, it is possible to select in order from the small region where the contrast is unclear (that is, the contrast range is narrow).

The image reconstructing unit 107 is a region dividing unit 103.
Based on the image data of the small region divided by the image processing unit 105 and the image data of the tomographic image generated by the image generation unit 110,
Generate new image data.

For example, the image is reconstructed only by the image of the small area designated by the area selecting unit 106 (in this case, some small areas are lost). Also, the image reconstruction unit 1
When the image processing is performed for each of the small areas designated by the area selection unit 106, 07 indicates to overwrite (overwrite) the image of each small area with the original tomographic image, or to replace the original tomographic image with the original tomographic image. It is possible to replace.

Next, the operation of the ultrasonic diagnostic apparatus 10 configured as above will be described. FIG. 13 is a flowchart showing an example of the overall processing flow of the ultrasonic diagnostic apparatus 10. First, the image generation unit 110 generates a tomographic image based on the ultrasonic echo received via the ultrasonic probe 11 and the transmission / reception unit 12 (S1301).

Next, the area dividing unit 103 uses the initial contour of the object specified by the instruction received from the operator via the operation unit 14 or automatically extracted by the contour extracting unit 111 (S1302). ), The tomographic image on the observation monitor is divided into a plurality of small areas (S130).
3).

Further, the evaluation value calculation unit 104 calculates an evaluation value for each of the small areas divided as described above (S130).
4). After that, the area-specific processing unit 105 performs image processing on each small area (S1305).

Then, when the area selection unit 106 selects a small area based on the above-mentioned evaluation value (S1306), the image reconstruction unit displays it on the monitor for observation based on the image of the selected small area. The tomographic image is reconstructed (S1307). The reconstructed tomographic image is output to the data output unit 16,
It is displayed on an observation monitor or the like.

FIG. 14 is a flow chart showing an example of the "area division process (S1303)" in FIG. First, the area dividing unit 103 calculates the center of gravity G of the initial contour specified as described above (S1401), and obtains the center line passing through the center of gravity G (S1402).

Next, the area dividing unit 103 specifies a dividing method (for example, the above dividing method) (S1403), and divides the tomographic image into a plurality of small areas according to the specified dividing method (S1404).

FIG. 15 is a flow chart showing an example of the "evaluation value calculation process" in FIG. Note that FIG.
Then, the case where the evaluation value “2α” related to the distribution of the brightness value is calculated is shown.

First, the evaluation value calculation unit 104 calculates the average value (YA) of the brightness values for all the pixels for which the evaluation value is to be calculated (S1501). Next, the evaluation value calculation unit 104 creates a histogram of brightness values centered on the average value of the brightness values for all pixels (S1).
502).

Further, the evaluation value calculation unit 104 initializes the increment α (α: natural number) of the luminance value (for example, α = 0) (S1503), and then the luminance value becomes “YA ± α”. The number of pixels is counted (S1504). After this, the evaluation value calculation unit 1
04 adds “1” to the value of α to update (S1505),
Whether or not the above counting result exceeds 80% with respect to the total number of pixels, that is, “YA ± α> 80%” (in this inequality
It is determined whether or not α ″ is a value before the update) (S1506). If this condition is satisfied, the evaluation value calculation unit 104 sets the evaluation value to “2α” (S1507).

FIG. 16 shows "region-specific processing" in FIG.
2 is a detailed flowchart of FIG. First, the region-specific processing unit 105 receives the specification of the image processing to be executed from the operator via the operation unit 14 (S1601). In this case, the image processing to be performed includes binarization processing, contrast conversion processing, bias conversion processing, noise removal processing, morphology processing, edge extraction processing, edge enhancement processing, and the like. Then, the region-specific processing unit 105 executes the specified image processing (S1602 to S1609). In addition,
As the default image processing, at least one (for example, edge enhancement processing) may be set to be executed.

FIG. 17 is a flow chart showing the details of the "image reconstruction process (S1307)" in FIG. First, the control unit 112 selects a small area to be reconstructed as an image from the operator via the operation unit 14 (S1).
701) and an instruction whether to overwrite the original image (S1702). When the instruction to overwrite is received (S1702: Yes), the control unit 112 overwrites the image of the selected small area on the tomographic image generated by the image generation unit 110 (S1703), and the image memory 101 is displayed. (S1704).

FIGS. 11 and 12 are diagrams showing the states of the tomographic images before and after the image processing is performed in the area-specific processing section 105. In FIG. 11, among the eight small regions divided by the region dividing unit 103, the small regions 310 and 330 have uniform low luminance values (that is, the entire image is dark), and thus the outline 1110 of the object. Is partly unclear. On the other hand, in the small region 360, on the contrary, the entire image has a uniformly high luminance value (that is, the entire image is whitish), and therefore a part of the outline 1110 of the object is unclear. Figure 1
2 is a region-specific processing unit 10 for the tomographic image of FIG.
Small area 3 which was unclear as a result of image processing by 5
It shows that the image quality of 10, 330, 360 is improved and the outline 1110 of the object becomes clear.

As described above, by using the ultrasonic diagnostic apparatus according to this embodiment, it is possible to reliably extract the contour of the object (for example, the left ventricle of the heart) even for a locally unclear image. It becomes possible to do it.

The image processing unit 1 according to the present embodiment
5 is configured as a part of the ultrasonic diagnostic apparatus, the image generation unit 110 of the image processing unit 15 is replaced with a data input unit capable of inputting image data from the outside, and image processing having the same function as the above is performed. It may be configured as a device.

The image processing unit 15 can handle image data (moving image data) continuously input in real time. In this case, each unit executes each processing for each frame.

As another example, when it is desired to extract the contour while tracking a part of the object in the tomographic image (for example, the annulus at the base of the valve that separates the left ventricle and the left atrium when extracting the left ventricle wall). If you want to trace the left interior wall while tracking a part), the tracking is performed as a process within a small area, while the image quality is improved as a process for a small area where the contour is unclear. By transmitting the position of the small area in which the annulus portion is present from the area selection unit, even if the image cannot be contour-extracted in the related art, tracking and extraction thereof can be performed.

As the above-mentioned image quality improvement, improvement of contrast by a histogram equalizer or noise cut, edge enhancement, etc. can be used. Of course, the method is not limited to this, and any method can be used.

For the above tracking, for example, pattern matching, autocorrelation between frames, detection of motion vector, etc. may be applied. Of course, the method is not limited to these, and any method may be used.

(Second Embodiment) In the first embodiment,
An example in which the present invention is applied to a tomographic image generated in an ultrasonic diagnostic apparatus has been described, but in the present embodiment, the present invention is applied to an image generated in an image processing apparatus such as a mobile phone with a camera function. An applied example will be described.

FIG. 18 is a block diagram showing the functional arrangement of the image processing apparatus 20 according to this embodiment. The image processing apparatus 20 has a function of improving the image quality according to the situation of the portion even if the image is locally unclear. The camera unit 21, the overall control unit 22, the operation unit 14, the image The processing unit 15 and the data output unit 16 are provided (for convenience, in the image processing apparatus 20, the functions of a general mobile phone with a camera function, such as the communication function and the memory function, are omitted).

Regarding the function of the image processing apparatus 20, a camera unit 21 is provided in place of the ultrasonic probe 11 in the ultrasonic diagnostic apparatus 10 according to the first embodiment, and a total control is provided in place of the transmitting / receiving unit 12. It is the same except that the portion 22 is provided. In the following, the first embodiment described above
Differences from the ultrasonic diagnostic apparatus 10 will be mainly described.

The camera section 21 is a section for photographing (for example, photoelectric conversion) and generating image data in accordance with an operator's operation input through the operation section 14, and includes a CCD or the like.

The overall control section 22 is a section for controlling the entire image processing apparatus 20, and comprises a CPU, ROM, RAM and the like. Further, the overall control unit 22 receives the image data generated by the camera unit 21 and stores the image data in the RAM or the like, and according to the operator's operation input via the operation unit 14, the received image data as it is or in the RA.
The image data read from M or the like is transmitted to the image processing unit 15. The functions of the operation unit 14, the image processing unit 15, and the data output unit 16 have the same functions as the corresponding units in the ultrasonic diagnostic apparatus 10 of the first embodiment.

FIGS. 19A to 19C show the image processing device 2.
FIG. 3 is a diagram showing a state up to area division of a captured original image at 0. FIG. 19A is an example of a captured original image. As shown in FIG. 19 (a), in the left corner of this original image, a portion that obstructs the original object to be photographed (for example, steam or smoke) is photographed, so the outline of the face becomes unclear. ing. FIG. 19 (b) shows FIG. 19 (a).
To the original image of the ultrasonic diagnostic apparatus 1 in the first embodiment
It is a figure which shows a mode that the initial contour was set by the method similar to 0.

FIG. 19 (c) is a diagram showing how the image of FIG. 19 (b) is divided into areas by the same method as in the first embodiment. 20A and 20B are diagrams showing a state in which image processing is performed on some of the small areas 23 and 24 selected from the divided small areas in the present embodiment. . Here, the small areas 23 and 24 in FIG. 20A show two small areas selected by the same method as in the first embodiment. FIG. 20B shows a small area 23,
It is a figure which shows a mode that the outline of the face was improved as a result of performing the image processing (for example, contrast conversion processing) to 24.

FIGS. 21 (a) and 21 (b) show a state in which the initial contour is set and the contour is extracted again for the image including a part of the small region subjected to the image processing in the present embodiment. FIG. FIG. 21A is a diagram showing a state in which the initial contour is set again in the image including the small regions 23 and 24 that have been subjected to the image processing. FIG. 21B is a diagram shown in FIG.
It is a figure which shows the result which performed finer contour extraction based on the image in which the initial contour of (a) was set. Figure 21 (b)
In this case, it is shown that a preferable contour is extracted along the contour of the actual face as shown in FIG.

FIG. 22 is a diagram for explaining an example of the function added to the image processing apparatus 20 described above. Figure 2
2B shows the result of contour extraction performed on the original image of FIG. In this case, the image processing device 20
As shown in FIG. 22D, allows the face to be “miniaturized” or “slimmed” based on the extracted contour. This "miniaturization" and "slimming" are shown in FIG.
As shown in (c), the vertical size reduction ratio (for example,
It can be realized by further reducing the reduction ratio of the lateral size with respect to 0.9) (for example, 0.7).

FIG. 23 is a diagram for explaining an example of other functions added to the image processing apparatus 20. As shown in FIG. 23, the image processing device 20 extracts a part of an image based on the extracted contour, combines the extracted image with another image (for example, an image of a landscape), and generates a new image ( For example, chromaki synthesis).

FIG. 24 is a flow chart showing the overall processing flow of the image processing apparatus 20. First, the image processing unit 15 generates an image based on the data received via the camera unit 21 and the overall control unit 22 (S23).
01).

Next, the overall control unit 22 specifies the initial contour of the object by an operator's operation or automatic extraction (S1302). As a result, the area dividing unit 103 divides the image on the display into a plurality of small areas (S13).
03). Subsequently, the overall control unit 22 receives the selection of the small area from the operator (S1306), and the processing unit for each area 1
05 is instructed to perform image processing for each small area (S
1305).

After that, the overall control unit 22 receives an instruction from the operator to again perform contour extraction (S23).
02: Yes), instructing each part to specify the initial contour and extract the contour of the object in the same manner as described above (S23).
03).

Further, the image processing unit 15 includes the overall control unit 2
By the instruction of 2, for the image obtained as described above,
Processing and synthesis are performed (S2304). In addition, in the above-described first and second embodiments, the area dividing method by the small area dividing unit is shown in FIGS. 3 to 7 and FIG. 19, but is not limited to these. For example, FIG. 25 (a) and FIG.
As shown in (b), for an image including a contour line,
The area may be divided such that the area tiles having a side length of “2c” are arranged from the reference point as a starting point. As a result, as shown in FIG. 25C, the image can be divided by tracing the contour line using eight area tiles. In this case, the area tiles are arranged so that the centers of the area tiles are on the contour line. Further, as another method of dividing the area, as shown in FIG. 26, the area sandwiched between the outer rectangle and the inner rectangle may be divided into small areas (area tiles). FIG. 26A defines a circumscribed rectangle (width W1 and length H1) that circumscribes the contour line first, and based on the shape of the circumscribed rectangle, the outer rectangle (width W2 and length H2) and the inner rectangle. (Width W3, length H3) is defined. Specifically, W2 =
5W1 / 3, H2 = 5H1 / 3, W3 = W1 / 3, H3 = H1
It is defined to be / 3. In FIG. 26 (b), an outer rectangle (width W4, length H) is first included so that the contour line is included therein.
4) is defined, and an inner rectangle (width W5, length H) is defined inside the contour line.
5) is defined. Specifically, W5 = W4 / 3, H5 = H3
It is defined to be / 3. Further, the area sandwiched between the outer rectangle and the inner rectangle is divided into area tiles (width W6, length H6). Specifically, the area tiles such that W6 = W4 / 3 and H6 = H4 / 6 are defined.

By accepting an instruction from the operator via the operation unit 14, the values of c, W1 to W6 and H1 to H6 are changed, and the area is divided by each method using the changed values. It may be that. Further, each of the above dimensions is an example, and it goes without saying that the image may be divided according to other dimensions.

As described above, by using the image processing apparatus according to the present embodiment, the same method as that of the ultrasonic diagnostic apparatus according to the first embodiment is used even for an image with an unclear contour such as a face. This makes it possible to extract the contour (ie to improve the image). In addition,
In the present embodiment, the face has been described as a target, but it goes without saying that the present invention can be applied to the case of extracting the contour of an arbitrary target.

[0100]

As described above, the image processing apparatus and the ultrasonic diagnostic apparatus according to the present invention divide an image into small areas and perform image processing according to the situation of each small area. It is possible to overcome the drawbacks that the automatic image quality adjustment function does not function sufficiently due to partial loss of the image and local low contrast and unclear edges in the device. Further, it is possible to overcome the drawback that the contour and boundary extraction methods do not function sufficiently due to the same cause.

That is, the conventional method for extracting contours and boundaries has the above-mentioned drawbacks because it is premised that the contours are clearly drawn. Since an image with low contrast can be sharpened, the quality of diagnostic medical treatment can be improved and the national medical cost can be reduced through the device.

[Brief description of drawings]

FIG. 1 is a block diagram showing an outline of a functional configuration of an ultrasonic diagnostic apparatus according to a first embodiment.

FIG. 2 is a block diagram showing a detailed functional configuration of an image processing unit in FIG.

FIG. 3 is a diagram for explaining a method of automatically extracting an initial contour of an object or specifying it by an operator's operation and radially dividing the center of gravity of the initial contour.

FIG. 4 is a diagram for explaining a modified example of the method shown in FIG.

FIG. 5 is a diagram illustrating a boundary line having a width of a certain number of pixels outside the specified initial contour, and radially dividing a donut-shaped region sandwiched between the initial contour and the boundary line at a predetermined angle. It is a figure for demonstrating a method.

FIG. 6 is a diagram for explaining a modified example of the method shown in FIG.

FIG. 7 is a diagram for explaining a method of equally dividing the lengths of the vertical axis and the horizontal axis of a tomographic image into N equal parts.

FIG. 8 is an example of how luminance values are distributed in a small area of a tomographic image.

FIG. 9A is a diagram showing a state of input luminance and output luminance in the binarization processing. (B) is a figure showing the relationship between an input brightness value and an output brightness value in contrast conversion processing and bias conversion processing.

FIG. 10 is a diagram showing an example of a method of converting luminance value dispersion.

FIG. 11 is a diagram schematically showing a state of a tomographic image before being subjected to image processing by a region-specific processing unit.

FIG. 12 is a diagram schematically showing a state of a tomographic image after being subjected to image processing by a region-specific processing unit.

FIG. 13 is a flowchart showing an example of the overall processing flow of the ultrasonic diagnostic apparatus.

FIG. 14 is a flowchart showing an example of “area division processing” in FIG.

15 is a flowchart showing an example of "evaluation value calculation processing" in FIG.

16 is a flowchart showing an example of "region-specific processing" in FIG.

FIG. 17 is a flowchart showing an example of “image reconstruction processing” in FIG.

FIG. 18 is a block diagram showing an outline of a functional configuration of an image processing apparatus according to a second embodiment.

FIG. 19A is an example of an original image taken by a mobile phone. FIG. 19B is a diagram showing how the initial contour is set in FIG. FIG. 19C is a diagram showing how the image of FIG. 19B is divided into regions.

20A is a diagram showing how a small area is selected from the images divided in FIG. 19C. FIG.
20B is a diagram showing a state in which image processing has been performed on the small area selected in FIG. 20A.

FIG. 21A is a diagram showing a state in which an initial contour is set in the image subjected to the image processing of FIG. 20B.
FIG. 21B is a diagram showing a state in which a fine contour is extracted based on the image of FIG.

FIG. 22 (a) is an example of an original image taken by a mobile phone. FIG. 22B is a diagram showing a state in which fine contours are extracted from the image of FIG.
(C) is a figure which shows the image at the time of "miniaturizing" the extracted face outline. (D) is a figure which shows a mode that the face was made "slim" and "small" based on the extracted face outline.

FIG. 23 is a diagram showing a state in which a face specified by the contour extraction is chroma-synthesized in a separately captured image.

FIG. 24 is a flowchart illustrating an example of the overall processing flow of the image processing apparatus.

FIG. 25A is a diagram showing reference points specified on a contour line. FIG. 6B is a diagram showing how area tiles are defined around the reference point of FIG.
FIG. 13C is a diagram showing how area tiles are defined for the entire image along the contour line based on the area tiles defined in FIG.

FIG. 26A is a diagram showing how an image is divided by area tiles defined based on a contour line, a circumscribed rectangle, an outer rectangle, and an inner rectangle. (B) is a contour line,
It is a figure which shows a mode that an image is divided | segmented by the area tile defined based on the outer rectangle and the inner rectangle.

FIG. 27A is an example of an original image taken by a conventional mobile phone. FIG. 27B is the above FIG. 27A.
It is a figure which shows a mode that the initial contour was set to. (C) is
This is a successful example when a fine contour is extracted based on the image of FIG. 27 (b).

FIG. 28A is another example of an original image captured by a conventional mobile phone. (B) is the above-mentioned FIG.
It is a figure which shows a mode that the initial contour was set to (a).
(C) is a failure example when a fine contour is extracted based on the image of FIG. 28 (b).

[Explanation of symbols]

10 Ultrasonic diagnostic equipment 11 Ultrasonic probe 12 Transmitter / receiver 13 Beat detector 14 Operation part 15 Image processing unit 16 Data output section 20 Image processing device 21 camera section 22 Overall control unit 101 image memory 102 general-purpose memory 103 area division unit 104 Evaluation value calculation unit 105 Area-specific processing unit 106 area selector 107 Image reconstruction unit 109 arithmetic unit 110 Image generator 111 contour extraction unit 112 control unit

   ─────────────────────────────────────────────────── ─── Continued front page    F term (reference) 4C601 BB02 EE09 JC06 JC07 JC08                       JC09 JC10 JC12 JC20 KK25                       LL23 LL38                 5B057 AA07 BA05 CA02 CA08 CA12                       CA16 CB08 CB12 CB16 CE02                       CE09 CE11 DC06 DC16                 5L096 AA06 BA06 BA13 EA35 EA43                       FA06 FA14 FA60

Claims (24)

[Claims] 1. An image acquisition means for acquiring image data, an area division means for dividing an image represented by the acquired image data into a plurality of small areas, and an area selection for selecting one or a plurality of the small areas. An image processing apparatus comprising: a unit; and a region-specific processing unit that performs image processing on each of the selected small regions. 2. The image data is image data relating to a tomographic image generated based on ultrasonic echoes, and the image acquisition means acquires the image data from an ultrasonic diagnostic apparatus. The image processing apparatus according to claim 1. 3. The image data is image data relating to an image captured by using a charge-coupled device, and the image acquisition means acquires the image data from a CCD camera. The image processing device described. 4. The image according to claim 1, wherein the area selecting unit performs the selection based on a distance between a reference point of the entire image and a reference point of each of the divided small areas. Processing equipment. 5. The image processing apparatus further comprises evaluation value calculation means for calculating an evaluation value indicating the clarity of the image for the divided small areas, and the area selection means includes the calculated evaluation value. The image processing apparatus according to claim 1, wherein the selection is performed based on a value. 6. The image processing apparatus according to claim 5, wherein the area selection unit selects small areas in order from a small area indicating that the evaluation value is not clear. 7. The evaluation value calculation means includes brightness value information, shape information, edge information, and 2 in a small area.
The image processing apparatus according to claim 5, wherein the evaluation value is calculated using at least one of the binarization information, the separation degree information, and the maximum / minimum luminance value information. 8. The area-specific processing means uses at least one of edge extraction processing, edge enhancement processing, binarization processing, contrast conversion processing, bias conversion processing, noise removal processing, and morphology processing as the image processing. The image processing apparatus according to claim 7, which is performed. 9. The image processing according to claim 8, wherein the area dividing unit performs the division by equally dividing the lengths of the X axis and the Y axis of the image by a predetermined number. apparatus. 10. The area dividing unit includes a contour information acquisition unit that acquires contour information indicating a contour of an object in the image, and a center of gravity calculation that calculates a center of gravity of a figure represented by the contour according to the acquired contour information. And a reference point identifying unit that identifies a reference point on the contour, based on a straight line connecting the center of gravity and the reference point, by radially dividing the image by a predetermined angle from the center of gravity, The image processing apparatus according to claim 8, wherein the division is performed. 11. The area dividing unit includes a contour information acquisition unit that acquires contour information representing a contour of an object in the image, a circumscribed rectangle setting unit that sets a rectangle circumscribing the contour, and a circumscribed rectangle of the circumscribed rectangle. An inner rectangle setting unit that sets an inner rectangle inside, an outer rectangle setting unit that sets an outer rectangle outside the circumscribing rectangle, and a region between the inner rectangle and the outer rectangle is divided based on the circumscribing rectangle. The image processing apparatus according to claim 8, further comprising: 12. The area dividing unit includes a contour information acquisition unit that acquires contour information representing a contour of an object in the image, a reference point identification unit that identifies a reference point on the contour, and the reference point. 9. The image processing according to claim 8, further comprising: a small area arranging unit that arranges a small area having a predetermined shape as a center on the contour, and the image includes the arranged small area. apparatus. 13. The image processing apparatus according to claim 12, wherein the area dividing unit further includes a small area changing unit that receives an instruction for changing the shape of the small area. 14. The image processing apparatus further comprises an image reconstructing unit that reconstructs an image using the image of the small area that has been subjected to the image processing. The image processing device according to item 1. 15. The image reconstructing means replaces an image corresponding to the small area in the acquired image with an image of the small area subjected to the image processing. Image processing device. 16. The image processing apparatus according to claim 15, further comprising a re-contour acquisition unit that acquires contour information indicating a contour of an object in the image after the replacement. 17. An image obtaining step of obtaining image data, an area dividing step of dividing an image represented by the obtained image data into a plurality of small areas, and an area selecting step of selecting one or a plurality of the small areas. An image processing method comprising: a step; and an area-specific processing step for performing specific image processing on each of the selected small areas. 18. An image obtaining step of obtaining image data, an area dividing step of dividing an image represented by the obtained image data into a plurality of small areas, and an image clarity of the divided small areas. An evaluation value calculation step of calculating an evaluation value indicating, an area selection step of selecting one or a plurality of the small areas based on the calculated evaluation value, and a specific image processing for each of the selected small areas. An image processing method, comprising: 19. A program for an image processing device, comprising: an image acquiring step of acquiring image data of the image; and an area dividing step of dividing an image represented by the acquired image data into a plurality of small areas. A program for causing a computer to execute an area selecting step of selecting one or a plurality of the small areas, and an area-specific processing step of performing specific image processing for each of the selected small areas. 20. A program for an image processing apparatus, comprising: an image acquisition step of acquiring image data of the image; and an area division step of dividing an image represented by the acquired image data into a plurality of small areas. An evaluation value calculation step of calculating an evaluation value indicating the clarity of the image for the divided small areas, and an area selection step of selecting one or a plurality of the small areas based on the calculated evaluation values. A program for causing a computer to execute a region-specific processing step of performing specific image processing for each of the selected small regions. 21. An ultrasonic diagnostic apparatus for displaying a tomographic image of a subject generated based on the reflection of ultrasonic waves, which is represented by image acquisition means for acquiring image data, and the acquired image data. Area dividing means for dividing the tomographic image into a plurality of small areas, area selecting means for selecting one or more of the small areas, and area-specific processing means for performing specific image processing for each of the selected small areas, An ultrasonic diagnostic apparatus comprising: a display unit that displays an image of the small area that has been subjected to the image processing. 22. An ultrasonic diagnostic apparatus for displaying a tomographic image of a subject generated based on the reflection of ultrasonic waves, which is represented by image acquisition means for acquiring image data, and the acquired image data. Area dividing means for dividing the tomographic image into a plurality of small areas, for the divided small areas, an evaluation value calculating means for calculating an evaluation value indicating the clarity of the image, based on the calculated evaluation value, Area selecting means for selecting one or a plurality of the small areas, area-specific processing means for performing a specific image processing for each of the selected small areas, An ultrasonic diagnostic apparatus comprising: an image reconstructing unit that reconstructs a tomographic image of a sample; and a display unit that displays the reconstructed tomographic image. 23. A program for an ultrasonic diagnostic apparatus for displaying a tomographic image of a subject generated based on the reflection of ultrasonic waves, the image acquiring step of acquiring image data, and the acquired image. A region dividing step of dividing the tomographic image represented by the data into a plurality of small regions, a region selecting step of selecting one or a plurality of the small regions, and a region to be subjected to specific image processing for each of the selected small regions. A program that causes a computer to execute a processing step and a display step for displaying an image of a small area subjected to the image processing. 24. A program for an ultrasonic diagnostic apparatus for displaying a tomographic image of a subject generated based on reflection of ultrasonic waves, the image acquiring step of acquiring image data, and the acquired image. An area dividing step of dividing the tomographic image represented by the data into a plurality of small areas, an evaluation value calculating step of calculating an evaluation value indicating image clarity for the divided small areas, and the calculated evaluation value Based on the area selection step of selecting one or more of the small areas, processing step by area to perform a specific image processing for each of the selected small areas, the image of the small area subjected to the image processing Characterized by causing a computer to execute an image reconstruction step of reconstructing a tomographic image of a subject using Program to be.