US-12626369-B2 - Gabor wavelet-fused multi-scale local level set ultrasonic image segmentation method

US12626369B2US 12626369 B2US12626369 B2US 12626369B2US-12626369-B2

Abstract

Disclosed is a Gabor wavelet-fused multi-scale local level set ultrasonic image segmentation method. In the method, non-uniformity of the grayscale of an ultrasonic image is taken as a texture having cluttered directions, the multi-directional property of Gabor wavelets is used to process the image, and intermediate images in different filtering directions are fused by taking maximum values, so as to obtain an intermediate image having a weakened texture effect and an enhanced difference between a foreground and a background. For the feature of a weak edge of an ultrasonic image, a concept of multi-scale is used to improve the conventional LIC method, Gaussian convolution kernels having different variances are set, and a final edge is obtained by means of average fusion.

Inventors

ZHIFENG ZHOU
Huiling Zou

Assignees

Beijing Lepulmage Technology Co., Ltd.

Dates

Publication Date: 20260512
Application Date: 20221021

Claims (5)

1 . An image segmentation method, comprising the following steps: step S1: obtaining an original image to be processed; step S2: performing filtering decomposition on the original image by using multi-directional Gabor wavelets, to obtain a plurality of intermediate images; step S3: fusing the plurality of intermediate images by taking maximum values, to obtain an enhanced image; step S4: constructing a corresponding level set energy equation for the intermediate images; step S5: optimizing parameters of the energy equation to enable an energy function to take a minimum value, to obtain an accurate position of an edge; and step S6: repeating step S5 until the energy function takes the minimum value to obtain a final edge; wherein a fusion equation for fusing the plurality of intermediate images in step S3 is as follows: I ′( x,y )=max{ I q ( x,y ), q= 0,1, . . . , Q− 1}, where I′(x,y) is the fused image data.
2 . The image segmentation method according to claim 1 , wherein a change function of the multi-directional Gabor wavelets in step S2 is: g q ( x,y )= g ( x cos θ+ y sin θ,− x sin θ+ y cos θ), wherein g(x,y) is a Gabor function in a two-dimensional space; g q (x,y) is a multi-directional Gabor wavelet transformation template, where x and y are coordinates in two directions of the template; θ=qπ/ , represents a total quantity of directions in wavelet transformation, and q is a direction parameter; and convolution is performed on Gabor wavelets in different directions with the original image to obtain the plurality of intermediate images, as shown by the following formula: I q ( x,y )= I ( x,y )* g q ( x,y ) q= 0,1, . . . Q− 1, wherein I(x,y) is original image data, and I q (x,y) is intermediate image data.
3 . The image segmentation method according to claim 1 , wherein the energy equation comprises: an energy functional item related to the image to be processed itself, a length regularization item for keeping edge smoothness by limiting an edge length, and a distance regularization item for keeping a level set equation from reinitialization.
4 . An image processing apparatus, comprising: a storage medium configured to store an image to be processed; and a processor configured to: obtain the image to be processed from the storage medium; perform filtering decomposition on the original image by using multi-directional Gabor wavelets, to obtain a plurality of intermediate images; fuse the plurality of intermediate images by taking maximum values, to obtain an enhanced image; construct a corresponding level set energy equation for the intermediate images; optimize parameters of the energy equation to enable an energy function to take a minimum value, to obtain an accurate position of an edge; and repeat optimizing the parameters of the energy equation until the energy function takes the minimum value to obtain a final edge.
5 . An ultrasonic imaging apparatus, comprising: an ultrasonic probe configured to emit an ultrasonic wave to an object under test, receive a reflected ultrasonic wave reflected by the object under test, and generate an echo signal corresponding to the reflected ultrasonic wave; a generation part configured to generate an ultrasonic image related to the object under test according to the echo signal; and a processing part configured to: obtain the image to be processed from the storage medium; perform filtering decomposition on the original image by using multi-directional Gabor wavelets, to obtain a plurality of intermediate images; fuse the plurality of intermediate images by taking maximum values, to obtain an enhanced image; construct a corresponding level set energy equation for the intermediate images; optimize parameters of the energy equation to enable an energy function to take a minimum value, to obtain an accurate position of an edge; and repeat optimizing the parameters of the energy equation until the energy function takes the minimum value to obtain a final edge.

Description

CROSS-REFERENCE TO RELATED APPLICATION This application is a continuation-in-part of International Application No. PCT/CN2020/107283, filed on Aug. 6, 2020, the entire contents of which is incorporated herein by reference. FIELD The present application relates to the field of ultrasonic image segmentation, and in particular to a Gabor wavelet-fused multi-scale local level set ultrasonic image segmentation method. BACKGROUND Currently, tumors in the digestive tract have high incidence worldwide and have a high fatality rate, which seriously threaten people's life and health. Ultrasonic endoscopy uses ultrasound to detect and image the internal organs of human body, which is non-damaging to human body and has low cost of detection and high diagnostic accuracy, and therefore becomes one of the important means for early detection and treatment of digestive tract tumors. The location, size, and shape of a tumor in the digestive tract are important parameters to assist physicians in judgment. Therefore, it is of great significance to perform edge extraction of tumors on ultrasonic images. Due to the detection characteristics of an ultrasound system, ultrasonic images have the disadvantages of uneven grayscale distribution, high noise, and poor edge continuity. As a result, it is difficult to obtain complete and accurate edges by using some classical segmentation methods such as a Canny operator method and a thresholding method. The proposal of an active contour model is a breakthrough in the field of image segmentation. The basic idea of the proposal is to use continuous curves to fit edges to be measured and to perform image segmentation by defining an energy functional equation with the edges as variables and taking minimum values. With the introduction of a level set method, the applicability of the active contour model has been developed. However, the conventional level set algorithm still has some defects when performing actual image processing. For example, the famous Mumford-Shah model is not appropriate for actual application due to the complex computational iteration process and massive computation. It is also difficult to obtain accurate segmentation results for images having non-uniform grayscale by using the Chan-Vase model improved based on this model. A local area-based level set method has been proposed for the segmentation of images having non-uniform grayscale. In 2007, Li proposed the local binary energy (local binary pattern, LBF) model, which introduced a Gaussian kernel function into an energy equation to extract local grayscale information, and added a regularization item, so that reinitialization is not required during the iteration of a level set, thereby reducing the amount of operations. However, the algorithm is sensitive to an initial contour and has poor robustness to noisy images. In 2008, Lankton proposed the LRB method, which also uses local region information to segment inhomogeneous images. However, the computational efficiency of the algorithm is low. In 2011, Li proposed the local intensity clustering level set method, which has a good segmentation effect for images with uniform grayscale variation but has serious over-segmentation for ultrasonic images with weak edges and uneven grayscale variation. Therefore, there is a need in this field for a method that can accurately segment ultrasonic images. SUMMARY For the foregoing technical problems in the prior art, an objective of the present application is to provide an ultrasonic image segmentation algorithm, thereby improving the segmentation accuracy of ultrasonic images. The technical solution of the present application is as follows. A first aspect of the present application provides an image segmentation method, including the following steps: step S1: obtaining an original image to be processed;step S2: performing filtering decomposition on the original image by using multi-directional Gabor wavelets, to obtain a plurality of intermediate images;step S3: fusing the plurality of intermediate images by taking maximum values, to obtain an enhanced image;step S4: constructing a corresponding level set energy equation for the intermediate images;step S5: optimizing parameters of the energy equation to enable an energy function to take a minimum value, to obtain an accurate position of an edge; andstep S6: repeating step S5 until the energy function takes the minimum value to obtain a final edge. Preferably, a change function of the multi-directional Gabor wavelets in step S2 is: gq(x,y)=g(x cos θ+y sin θ,−x sin θ+y cos θ), where g(x,y) is a Gabor function in a two-dimensional space; gq(x,y) is a multi-directional Gabor wavelet transformation template, where x and y are coordinates in two directions of the template; θ=qπ/, represents a total quantity of directions in wavelet transformation, and q is a direction parameter; and convolution is performed on Gabor wavelets in different directions with the original image to obtain the