Pathological image color quality comprehensive evaluation method based on color separation

Abstract

The invention relates to a pathological image color quality comprehensive evaluation method based on color separation. The method includes step 1, performing color separation on pathological images; step 2, utilizing four indicators including red-blue contrast ratio, color deviation degree, color information entropy and gap area fragmentation degree; step 3, utilizing a sample set to train to acquire a neural network model, using the model to comprehensively evaluate a testing set, comparing an evaluation value with a truth value, and calculating accuracy. By the method, a gap in pathological image color quality evaluation is filled; a good evaluation result is acquired by utilizing color separation and a neutral network, and the method has wide application prospect in the field of pathological image quality evaluation.

Description

Technical field: [0001] The invention relates to a method for comprehensively evaluating color quality of pathological images based on coloring separation, and belongs to the technical field of pathological image quality evaluation. Background technique: [0002] Pathological images are digitized images of pathological slides. Pathology is not only a basic medical subject, but also a highly practical subject with a clinical nature, which is called diagnostic pathology. Clinically, some diseased tissues or organs are treated with various chemicals and embedding methods to make them fixed and hardened, cut into thin slices on a microtome, adhered to glass slides, and dyed with various colors to obtain pathological results. slice. Examine the pathological section under a microscope and observe the changes in the morphological structure, metabolism and function of the relevant parts of the diseased body, which can not only obtain important theoretical knowledge for research an...

AI Technical Summary

Problems solved by technology

However, unlike other pathological image analysis techniques, the research on the quality evaluation of pathological images by scientific and technological workers at home and abroad is still very weak.

At present, there are no reference image quality evaluation methods based on local structure information measurement for Gaussian blur and Gaussian white noise in the quality evaluation of pathological images. There are few studies on the color quality evaluation of pathological images.

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