Method for analyzing histopathologic image and system thereof

Abstract

The invention provides a method for analyzing a histopathologic image and a system thereof. The method comprises the steps that the central position of a cell nucleus is detected; then the graph structure features of the cell nucleus distribution are extracted; one or multiple graph structure features is / are calculated so that the global features of the cell nucleus distribution are obtained; the local features are directly extracted near the position of the cell nucleus; the global feature and the local features are fused so as to obtain the fused features; and the fused features are classified by using a classifier. The system comprises a high-resolution pathologic section scanner which scans the dyed histopathologic section to a computer so as to obtain a high-resolution digital histopathologic image; and the computer which performs the steps. The central position of the cell nucleus can be rapidly, accurately and automatically detected so that redundant information can be reduced, the overall distribution situation of the position of the cell nucleus and the detailed situation near the cell nucleus can be both considered, pathologic grading of the section can be accurately and rapidly realized and thus processing and transmission are rapid and resource consumption is low.

Description

technical field [0001] The invention relates to a computer-aided medical image processing technology, in particular to a method and system for analyzing histopathological images. Background technique [0002] Cancer is generally a heterogeneous disease with different risk grades, and its corresponding treatment options and prognosis vary from case to case. Taking the diagnosis of breast cancer as an example, mammography is usually used to determine the nature of the lesion first, and then the living tissue is obtained by puncture and stained to make a sample, and the stained sample is imaged to obtain a histopathological image. Most of the research on histopathological images focuses on the identification of specific tissue structures, such as cell nucleus detection, classification of malignant and benign pathological tissues, etc. The location, size, shape, and some specific structures of these tissues are very important indicators for disease diagnosis. The generation an...

AI Technical Summary

Problems solved by technology

However, the biggest defect of the SIFT feature is that it cannot accurately extract local features near the nucleus. Once the SIFT feature is extracted on the entire picture, it may contain a large number of SIFT feature points taken from the image background rather than near the nucleus, so a lot of redundancy is introduced. remaining information,

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