WO2018176319A1 - Ultrasound image analysis method and device - Google Patents

Abstract

An ultrasound image analysis method and device, for segmenting a target region from an ultrasound image. The analysis method comprises: pre-processing (101) the ultrasound image; extracting an initial contour of the target region from the pre-processed ultrasound image (102); and evolving the initial contour of the target region to acquire an accurate boundary of the target region (103). By means of the method and device, while the accuracy of a segmentation result is ensured, the automation level and speed of the segmentation can be improved.

Description

Ultrasonic image analysis method and device Technical field

The present invention relates to the field of image processing technologies, and in particular, to a method and an apparatus for analyzing an ultrasound image.

Background technique

Clinically, CAD (Computer-aided Diagnosis) system is used to assist the diagnosis of the disease, which can avoid the influence of subjective factors and improve the accuracy and objectivity of the diagnosis results. Therefore, the computer-aided diagnosis system is increasingly Used in clinical diagnosis. In computer-aided diagnostic systems, diagnostic ultrasound results are typically provided by analyzing medical ultrasound images; for example, for ultrasound images of breast tumors, the ultrasound image is analyzed using a CAD system to provide diagnostic results for breast tumors, tumor location, or size. . For computer-aided diagnosis systems, how to segment the lesion area (such as the tumor area) from the ultrasound image is a key link.

However, the current segmentation algorithm for ultrasound images is generally semi-automatic, that is, the clinician needs to manually select a representative point of the region of interest or the region of interest, and then perform segmentation by computer. The semi-automatic segmentation method reduces the automation performance of the CAD system and cannot meet the needs of the massive medical image processing that is increasingly occurring in the clinic. At present, most of the automatic segmentation algorithms introduce some a priori constraint information such as shape, texture and spatial relative position to achieve fully automatic segmentation. However, due to the size and shape of the lesions and the unclear boundaries, and even if the same patient is scanned by different ultrasound equipment, the results may be different, so it is difficult to accurately extract the prior constraint information of the breast tumor, thus affecting the analysis. The accuracy of the results.

Therefore, in the existing CAD system, the segmentation method adopted for medical ultrasound images exists. It is difficult to balance both efficiency and accuracy.

Summary of the invention

The embodiment of the invention provides a method and a device for analyzing an ultrasonic image, which can improve the automation level and speed of the segmentation while ensuring the accuracy of the segmentation result.

An embodiment of the present invention provides an analysis method of an ultrasound image for segmenting a target region from the ultrasound image, the analysis method comprising: preprocessing the ultrasound image; and the pre-processed ultrasound An initial contour of the target area is extracted from the image; an initial contour of the target area is evolved to obtain an accurate boundary of the target area.

The step of pre-processing the ultrasound image includes: selecting an effective region of the ultrasound image; and performing denoising processing on the selected effective region.

The step of extracting the initial contour of the target region from the pre-processed ultrasound image comprises: pre-segmenting the pre-processed ultrasound image to obtain a pre-segmented image; The pre-segmented image sequentially performs a series of processes, wherein the series of processes includes at least one of: morphological processing, void filling, and removing an area connected to the boundary; extracting through the series of processed images An enclosed area; determining an initial contour of the target area based on the size of the extracted closed area.

The step of pre-segmenting the pre-processed ultrasound image includes: processing the pre-processed ultrasound image by using a maximum inter-class variance method to obtain a first threshold; a threshold value, a foreground image is segmented from the pre-processed ultrasound image; the foreground image is processed by the maximum inter-class variance method to obtain a second threshold; and the second threshold is used The pre-processed ultrasound image is pre-segmented to obtain the pre-segmented image.

The step of pre-segmenting the pre-processed ultrasound image includes: processing the pre-processed ultrasound image by using a maximum inter-class variance method to obtain a first threshold; a threshold value for pre-segmenting the pre-segmented ultrasound image to obtain the pre-segmented image; the step of sequentially performing a series of processing on the pre-segmented image includes: The shape-processed processing is performed a plurality of times on the pre-segmented image.

The step of determining an initial contour of the target region according to the size of the extracted closed region includes: for the extracted closed region, retaining n closed regions having the largest area, where n is an integer greater than 0 And determining an initial contour of the target area according to the size relationship of the n closed areas.

The step of determining an initial contour of the target area according to the size relationship of the n closed areas includes: when n=2, if the area of the larger closed area is smaller and smaller in the two closed areas If the difference between the areas of the regions is less than a preset threshold, the two closed regions are simultaneously determined as the initial contour of the target region; if the difference between the area of the larger closed region and the area of the smaller closed region is greater than the pre- A threshold is set to determine the larger enclosed area as the initial contour of the target area.

Wherein, the step of evolving the initial contour of the target area comprises: using an area-based active contour model to evolve an initial contour of the target area.

Wherein, in the region-based active contour model, the edge of the target away from the evolution curve is detected by the edge indication function to guide the evolution curve to stop at the boundary of the target contour, thereby improving the convergence speed;

The edge indication function is:

或者，

or,

Where α∈(0,1) is the control coefficient of the image boundary intensity field versus evolution velocity, and β is the proportional constant. R is the edge strength obtained by the exponentially weighted average ratio operator.

An embodiment of the present invention provides an ultrasound image analysis apparatus for segmenting a target area from the ultrasound image, the analysis apparatus comprising: a preprocessing module for preprocessing the ultrasound image; an initial contour An extraction module, configured to extract an initial contour of the target region from the pre-processed ultrasound image; and an evolution module configured to evolve an initial contour of the target region to obtain an accuracy of the target region boundary.

The beneficial effects of the embodiments of the present invention are:

In the embodiment of the invention, the automation level and speed of the segmentation can be improved while ensuring the accuracy of the segmentation result.

DRAWINGS

1 is a flow chart showing an embodiment of an analysis method of an ultrasonic image of the present invention;

2a is a schematic flow chart of an embodiment of step 101 in FIG. 1;

Figure 2b is a schematic illustration of an embodiment of an original ultrasound image and an active area;

3 is a schematic flow chart of an embodiment of step 102 in FIG. 1;

Figure 4a and Figure 4b are schematic diagrams showing the evolution results of the conventional CV model and the improved CV model, respectively;

Fig. 5 is a view showing the configuration of an embodiment of an ultrasonic image analyzing apparatus of the present invention.

detailed description

The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It is understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

FIG. 1 is a schematic flow chart of an embodiment of an analysis method of an ultrasonic image of the present invention. The method for analyzing the ultrasound image can be integrated into the CAD system for segmenting the target region from the ultrasound image, thereby facilitating the CAD system to provide auxiliary diagnostic data. Wherein, the ultrasound image may be, for example, a breast tumor ultrasound image, but the invention is not limited thereto. As shown in FIG. 1, the analysis method of the ultrasonic image includes the following steps:

Step 101: Preprocess the ultrasound image. Among them, the ultrasound image can be generated, for example, by an ultrasound device and then input to a CAD system for processing.

Step 102: Extract an initial contour of the target area from the pre-processed ultrasound image. Wherein, the target area may be, for example, a tumor area in an ultrasound image of a breast tumor.

Step 103: Evolve the initial contour of the target area to obtain an accurate boundary of the target area.

In this embodiment, by preprocessing the ultrasound image, extracting the initial rotation of the target region, and evolving the initial rotation, the automation level and speed of the segmentation can be improved while ensuring the accuracy of the segmentation result.

The respective steps of Fig. 1 will be described in turn below.

As shown in FIG. 2a, it is a schematic flowchart of an embodiment of step 101 in FIG. It includes the following steps:

Step 201: Select an effective area of the ultrasound image.

Wherein, in step 201, the manual cutting method may be used to select the effective area. The so-called manual cutting method manually uses the mouse to draw a box to trim the original image, thereby removing the surrounding irrelevant area and retaining the center of the image. region. In addition, the program setting method can also be used to select the effective area; because the image size and the frame size collected by the ultrasound device of the same model are fixed, it is possible to reserve only the pixels of a specific size area in the image by program setting, thereby ignoring the surrounding area. Irrelevant information.

For example, as shown in FIG. 2b, the original ultrasound image and the image of the effective region obtained after the processing in step 201; wherein the left image in FIG. 2b is the original ultrasound image, and the image on the right is the image of the effective region. .

Step 202: Perform denoising processing on the selected effective area.

Wherein, in step 202, the image may be filtered by a SRAD (Speckle Reducing Anisotropic Diffusion) model to remove speckle noise. Among them, the SRAD model can effectively filter out the ultrasonic image speckle noise, simplify the image structure, improve the image quality, and maintain the detail information at the edge of the image.

As shown in FIG. 3, it is a schematic flowchart of an embodiment of step 102 in FIG. 1, which includes the following steps:

Step 301: Perform pre-segmentation on the pre-processed ultrasound image to obtain a pre-segmented image.

Wherein, in step 301, the pre-segmented ultrasound image is pre-segmented by using two maximum inter-class variance methods. It should be noted that the details of the maximum inter-class variance method are familiar to those skilled in the art, and therefore are not described herein.

Specifically, in step 301, the pre-processed ultrasound image is first processed using a maximum inter-class variance method to obtain a first threshold (ie, a global threshold). Then, according to the first threshold, the preprocessed ultrasound image is divided into a background image and a foreground image, wherein the foreground image may include a target region. Then, the foreground image is processed using the maximum inter-class variance method to obtain a second threshold (optimum threshold). Finally, the pre-segmented ultrasound image is pre-segmented by using the second threshold to obtain a pre-segmented image. It should be noted that the pre-segmented image may be a binarized image.

Optionally, in step 301, the maximum inter-class variance method may be performed only once, that is, the pre-processed ultrasound image is first processed by using a maximum inter-class variance method to obtain a global threshold. Then use this The global threshold is used to pre-segment the pre-processed ultrasound image to obtain a pre-segmented image.

It should be noted that, in step 301, when the maximum inter-class variance method is used twice, the mis-segmentation can be reduced. When the maximum inter-class variance method is used, in order to improve the accuracy, the number of morphological processing can be increased in a series of subsequent processing.

Step 302: Perform a series of processing on the pre-segmented image, wherein the series of processing includes at least one of the following: morphological processing, hole filling, and removing the area connected to the boundary.

The morphological processing may mean that the binarized image obtained after the pre-segmentation often contains some noise fragments caused by speckle noise. Therefore, in order to filter out small burrs and isolated points in the image, cut off the elongated connection, and smooth the edge of the lesion (such as a tumor), the binarized image (ie, the pre-segmented image) is subjected to morphological processing. At the same time, in order to achieve better results, larger structural elements can be used for corrosion and smaller structural elements for expansion.

Among them, due to residual noise, uneven gray scale inside the lesion, etc., there will be “cavities” inside the lesion after binarization, so it is necessary to fill the small holes in the closed area.

Among them, the area connected to the boundary in the binarized image is often mis-segmented by artifacts, not the real lesion area, so in order to eliminate the influence, the area connected to the boundary in the binarized image is removed.

Step 303: Extract a closed area in the series of processed images.

Among them, after the processing of the foregoing steps, some closed connected regions (generally black) appear in the image, which are tumor candidate regions. In the step, all closed areas are extracted and can be sorted according to the size of each closed area.

Step 304: Determine an initial contour of the target area according to the size of the extracted closed area.

Wherein, in step 304, for the extracted closed area, the n closed areas having the largest area are reserved, wherein n is an integer greater than 0; and the target area is determined according to the size relationship of the n closed areas The initial outline of the field. It should be noted that if there is only one closed area, the closed area is directly used as the initial outline of the target area. The following describes the process with n=2 as an example:

First, for the extracted closed area, the two closed areas with the largest area are extracted. If the difference between the area of the larger closed area and the area of the smaller closed area is less than a preset threshold, the two closed areas are simultaneously determined as targets. The initial contour of the region; if the difference between the area of the larger enclosed area and the area of the smaller enclosed area is greater than a predetermined threshold, only the larger enclosed area is determined as the initial contour of the target area. Wherein, the preset threshold may be half of the area of the larger closed area or other values.

In this embodiment, through the above processing, the initial contour of the target area can be automatically extracted without manually setting the initial contour, so that the automation level of the CAD system can be improved.

In step 103 of FIG. 1, the region-based active contour model is mainly used to evolve the initial contour of the target region, thereby obtaining an accurate boundary of the target region. Among them, the region-based active contour model, also known as CV model, is an energy model based on image global region information proposed by Chan and Vese in 2001. The core idea is to use segmentation constants to approximate various parts of the image. The variational method is used to introduce the level set to establish the equation, and the differential method is used for numerical calculation. Finally, the evolution problem of the boundary contour is transformed into the energy minimization problem.

In the embodiment of the present invention, instead of directly adopting the CV model, the CV model is improved to improve the convergence speed. Specifically, in the embodiment of the present invention, for the traditional CV model, the embodiment of the present invention detects the far-away evolution by adding an edge indication function (specifically, replacing the Dirac function in the traditional CV mode with an edge indication function). The target edge of the curve is used to guide the evolution curve to stop at the boundary of the target contour, thereby increasing the convergence speed.

The edge indication function can be expressed as:

或者，

or,

Where α∈(0,1) is the control coefficient of the image boundary intensity field versus evolution rate, β is the proportional constant, and R is the edge intensity obtained by the ratio of exponentially weighted averages (ROEWA) operator. Here, the gradient value adopted by the ROEWA operator instead of the general image segmentation algorithm is that the speckle noise in the ultrasound image is multiplicative noise, and the ROEWA operator can better adapt to the image in which the noise is a multiplicative model. Wherein, the constructed edge indication function is a monotonically decreasing function. When R→∞, the value of g(R)→0, g tends to be 0, indicating that the closer to the true boundary contour of the lesion. Therefore, the edge indication function itself can also speed up the convergence of the CV model.

In addition, to add, after the above improvements, the energy function of the CV model is:

Where u ₀ ≥ 0, ν ≥ 0, λ ₁ , λ ₂ > 0, and is a given parameter.

In step 103 of FIG. 1, by improving the conventional CV model, the convergence speed of the CV model can be improved, thereby speeding up the processing. For example, as shown in the following table, the split time comparison table of the improved CV model and the conventional CV model at different iteration times. It can be seen from the table that the segmentation time of the improved CV model is significantly faster than the traditional CV model.

For another example, for the same ultrasound tumor image. In the case of 500 iterations, the results of evolution using the traditional CV model are shown in Fig. 4(a), and the results of the evolution using the improved CV model are shown in Fig. 4(b). It can be seen from the figure that the results of the evolution using the improved CV model are closer to the true boundary of the tumor.

The image analysis method of the embodiment of the present invention has been described in detail above, and the apparatus corresponding to the method will be described below. It should be noted that since the relevant details have been previously described, only the main structure of the device will be described below, and the detailed description thereof will be omitted.

As shown in FIG. 5, it is a schematic structural view of an embodiment of an analysis apparatus for an ultrasonic image. The analysis device 500 can be used to segment a target region from an ultrasound image. The analysis device 500 includes: a pre-processing module 501 for pre-processing the ultrasound image; and an initial contour extraction module 502, configured to extract an initial of the target region from the pre-processed ultrasound image An outline; and an evolution module 503, configured to evolve an initial contour of the target area to obtain an accurate boundary of the target area.

The ultrasonic image analyzing device of the embodiment of the invention can improve the automation level and speed of the segmentation while ensuring the accuracy of the segmentation result.

One of ordinary skill in the art can understand that all or part of the process of implementing the foregoing embodiments can be completed by a computer program to instruct related hardware, and the program can be stored in a computer readable storage medium. When executed, the flow of an embodiment of the methods as described above may be included. The storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

The above is only the preferred embodiment of the present invention, and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection of the present invention. Within the scope.

Claims (10)

An analysis method for an ultrasound image for segmenting a target region from the ultrasound image, wherein the analysis method comprises: Pre-processing the ultrasound image; Extracting an initial contour of the target region from the pre-processed ultrasound image; An initial contour of the target area is evolved to obtain an accurate boundary of the target area. The method for analyzing an ultrasonic image according to claim 1, wherein the step of preprocessing the ultrasonic image comprises: selecting an effective region of the ultrasound image; and performing denoising processing on the selected effective region . The method for analyzing an ultrasonic image according to claim 1, wherein the extracting the initial contour of the target region from the preprocessed ultrasound image comprises: Pre-segmenting the pre-processed ultrasound image to obtain a pre-segmented image; Performing a series of processes on the pre-segmented image, wherein the series of processes includes at least one of: morphological processing, void filling, and removing an area connected to the boundary; Extracting a closed region in the series of processed images; An initial contour of the target area is determined according to the size of the extracted closed area. The method for analyzing an ultrasound image according to claim 3, wherein the step of pre-segmenting the pre-processed ultrasound image comprises: Using the maximum inter-class variance method to process the pre-processed ultrasound image to obtain a first threshold; Deriving a foreground image from the pre-processed ultrasound image according to the first threshold; Processing the foreground image using the maximum inter-class variance method to obtain a second threshold; And pre-segmenting the pre-processed ultrasound image by using the second threshold to obtain the pre-segmented image. The method for analyzing an ultrasound image according to claim 3, wherein the step of pre-segmenting the pre-processed ultrasound image comprises: Processing the pre-processed ultrasound image using a maximum inter-class variance method to obtain a first threshold; Pre-segmenting the pre-processed ultrasound image according to the first threshold to obtain the pre-segmented image; The step of sequentially performing a series of processing on the pre-segmented image includes performing the shape processing on the pre-segmented image multiple times. The method for analyzing an ultrasonic image according to claim 3, wherein the step of determining an initial contour of the target region according to the size of the extracted closed region comprises: For the extracted enclosed area, the n closed areas having the largest area are reserved, where n is an integer greater than 0; An initial contour of the target area is determined according to a size relationship of the n closed areas. The method for analyzing an ultrasonic image according to claim 6, wherein the determining the initial contour of the target region according to the size relationship of the n closed regions comprises: When n=2, if the difference between the area of the larger closed area and the area of the smaller closed area is less than a preset threshold in the two closed areas, the two closed areas are simultaneously determined as the initial of the target area. a contour; if the difference between the area of the larger enclosed area and the area of the smaller enclosed area is greater than a predetermined threshold, determining the larger enclosed area as an initial contour of the target area. The method of analyzing an ultrasonic image according to claim 1, wherein the step of evolving the initial contour of the target area comprises: The region-based active contour model is used to evolve the initial contour of the target region. The method for analyzing an ultrasonic image according to claim 8, wherein in the region-based active contour model, a target edge away from the evolution curve is detected by an edge indicating function to guide the evolution curve to stop at the target contour At the boundary, thereby increasing the rate of convergence; The edge indication function is:

or,

Among them, α∈(0,1) is the control coefficient of the image boundary intensity field versus evolution velocity, β is the proportional constant, and R is the edge intensity obtained by the exponential weighted average ratio operator. An apparatus for analyzing an ultrasound image for segmenting a target area from the ultrasound image, wherein the analysis apparatus comprises: a preprocessing module for preprocessing the ultrasound image; An initial contour extraction module, configured to extract an initial contour of the target region from the preprocessed ultrasound image; An evolution module is configured to evolve an initial contour of the target area to obtain an accurate boundary of the target area.

Images