Detection method and device of mammary image lesion area and computer storage medium

A lesion area and detection method technology, which is applied in the field of medical image processing, can solve the problems of inability to accurately segment dense breast images, poor detection result accuracy, and inability to obtain tumor edges.

Active Publication Date: 2018-04-24
深圳蓝影医学科技股份有限公司
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Problems solved by technology

Among them, the first) method is only based on the feature of breast lesion area density, which is not good for the detection of lesion areas in dense breast images, and the second) method only relies on the K-means clustering algorithm to extract interest Regions, the segmentation effect is better for circular or quas

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  • Detection method and device of mammary image lesion area and computer storage medium
  • Detection method and device of mammary image lesion area and computer storage medium
  • Detection method and device of mammary image lesion area and computer storage medium

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Embodiment Construction

[0050] It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0051] In the prior art, the processing methods for dense breast images mainly include the following two types: 1) by dividing the dense breast images into several sub-regions, and extracting the density features of each sub-region, performing cluster analysis, and finally displaying Clustering results; 2) Find the region of interest in the mammary image by the K-means method, and then extract features that characterize the mass to distinguish the mass from normal tissue. Among them, the first) method is only based on the feature of breast lesion area density, which is not good for the detection of lesion areas in dense breast images, and the second) method only relies on the K-means clustering algorithm to extract interest Regions, the segmentation effect is better for circular or quasi-circular lesions with clearer ...

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Abstract

The invention discloses a detection method of a mammary image lesion area. The method comprises a step of receiving a mammary image to be detected and preprocessing the mammary image to be detected, astep of carrying out primary cluster segmentation on preprocessed mammary image based on a Nystrom spectral clustering algorithm to obtain a suspicious mammary lesion area, a step of carrying out secondary cluster segmentation on the suspicious mammary lesion area based on a K-means clustering algorithm to obtain a corresponding interested area, and a step of extracting feature information of theinterested area and detecting whether the interested area is a mammary lesion area according to the feature information. The invention also discloses a detection device of a mammary image lesion areaand a computer storage medium. The accuracy of lesion area segmentation in the mammary image can be improved, and thus the accuracy of a detection result of the mammary lesion area is improved.

Description

technical field [0001] The present invention relates to the technical field of medical image processing, in particular to a method, device and computer storage medium for detecting lesion areas of mammary gland images. Background technique [0002] Breast cancer is a common malignant tumor, early diagnosis and treatment is the key to reduce breast cancer mortality. The computer-aided detection system can help doctors make final diagnostic decisions by detecting suspicious lesion areas, thereby improving the survival rate and quality of life of breast cancer patients. Since lumps and calcification clusters are the most common imaging signs of breast cancer, the automatic detection of lumps and calcifications has also become the two main aspects of the computer-aided diagnosis system. Among them, tumors have always been a difficulty in computer-aided detection due to factors such as blurred edges, various shapes, and low contrast with surrounding tissues. Especially for dens...

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Application Information

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IPC IPC(8): G06T7/00G06T7/11G06K9/62
CPCG06T7/0012G06T7/11G06T2207/30068G06F18/2321
Inventor 郑杰胡阳陈晶郭朋
Owner 深圳蓝影医学科技股份有限公司
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