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Medical image segmentation method based on correlation matrix self-learning and explicit rank constraint

An association matrix, medical image technology, applied in the field of computer vision, can solve the problem of discount of segmentation effect, and achieve the effect of satisfying accuracy and obvious diagonal structure

Active Publication Date: 2017-07-14
ZHEJIANG UNIV OF TECH
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Problems solved by technology

Due to the imaging principle of medical images and the differences in human tissue itself, many traditional image segmentation algorithms are susceptible to noise or light, and the segmentation effect is greatly reduced due to the multi-dimensionality of the image.

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  • Medical image segmentation method based on correlation matrix self-learning and explicit rank constraint
  • Medical image segmentation method based on correlation matrix self-learning and explicit rank constraint
  • Medical image segmentation method based on correlation matrix self-learning and explicit rank constraint

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Embodiment Construction

[0015] The present invention will be further described below.

[0016] The specific method includes the following steps (such as figure 1 shown):

[0017] Step 1: The CT machine takes the CT image of the human body, obtains the DICOM file, analyzes and reads the pixel data of the image. Modify the gray level of the image data, and then perform the histogram equalization operation to enhance the contrast of the image area components and make the image details clearer. Filter the target segmentation area, use the human-computer interaction function roipoly in MATLAB to formulate the polygon of the target area, and use the filter to filter until the CT image achieves the most suitable segmentation effect.

[0018] Step 2, divide the preprocessed image pixel data into n pixel blocks x in sequence, each pixel block x contains m pixel elements, and merge them into an m×n dimensional matrix X=[x 1 ,x 2 ,...,x n ]∈R m×n The input is based on the algorithm model of correlation ma...

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Abstract

The invention provides an image segmentation method based on correlation matrix self-learning and an explicit rank constraint. Low rank representation, similarity learning and a cluster structure constraint are incorporated into the same frame and are applied to medical CT image segmentation. The method comprises the steps that (1) a CT original image is preprocessed; the gray scale is used to correct and enhance the image; histogram equalization is carried out; and a median filter method is used to filter a target area to reduce interference and noise in the image; (2) an algorithm based on correlation matrix self-learning and the explicit rank constraint is used to solve the eigenvalue projection matrix of the image; and (3) a traditional clustering algorithm is used to cluster and split a projection matrix, and a region of interest is calibrated.

Description

technical field [0001] The invention relates to a medical image segmentation method, in particular to a medical image segmentation method based on an association matrix self-learning and an explicit rank constraint data representation clustering algorithm, belonging to the field of computer vision. Background technique [0002] Medical image segmentation is a process of dividing an image into several regions according to the similarity and difference between regions. Computed tomography (abbreviated as CT) is a fast and reliable medical examination method. The segmentation of organ structure and tissue in medical CT images is the basis for quantitative analysis of lesions and diagnosis of complex diseases. [0003] Medical CT images have the characteristics of complex textures and blurred edges. Selecting and optimizing image segmentation algorithms will help doctors analyze images and diagnose diseases more clearly and accurately. Due to the imaging principles of medical i...

Claims

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Application Information

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IPC IPC(8): G06T7/11G06T5/00G06K9/62
CPCG06T2207/30061G06T2207/10081G06T2207/20081G06F18/23213G06T5/70
Inventor 郑建炜鞠振宇邱虹杨平康帆陈婉君王万良
Owner ZHEJIANG UNIV OF TECH
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