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

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...

AI Technical Summary

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 quasi-circular lesions with clearer edges and more regular shapes, but for lesions with irregular shapes and hidden in dense tissue regions, more accurate tumor edges cannot be obtained.

Therefore, the existing technology cannot accurately segment the lesion area in the dense breast image, resulting in poor detection results

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