Color Deconvolution Method and Segmentation Method of Microscopic Image Based on Non-negative Matrix Factorization

Abstract

The invention discloses a microscopic image color convolution removal method based on non-negative matrix factorization (NMF), and belongs to the image information processing technology filed. The microscopic image color convolution removal method based on the NMF is directed at a tissue microscopic image after dye marking, and uses an NMF method to separate observation channels corresponding to different coloring agents. The invention further discloses a microscopic image cutting method based on the NMF. The microscopic image cutting method based on the NMF is used to cut the image based on the observation channels separated through the NMF method, not only can achieve rapid and completely automatic image processing, but also obtain an accurate image cutting result, and provides accurate basis for subsequent cell detection and pathological diagnostic analysis. Compared with the prior art, the microscopic image color convolution removal method and the microscopic image cutting method based on the NMF can effectively improve microscopic image cutting accuracy, save computation time, and obtain the microscopic image good in visual effect.

Description

technical field [0001] The present invention relates to the technical field of image information processing, in particular to a microscopic image color deconvolution method and segmentation method based on Nonnegative Matrix Factorization (NMF for short). Background technique [0002] Pathology is the microscopic study of the morphological properties of cells. It plays a subjective and important role in the decision-making of treatment options. Especially in the diagnosis of some diseases including cancer, the analysis results of pathological images are still considered as the 'gold standard'. Pathology researchers have recognized the importance of quantitative analysis of pathological images. It can be used to support a clinician's diagnostic decision about a disease, and it can also help evaluate the treatment effect of a patient's disease. Quantitative analysis is not only crucial in the clinical field, but also in the field of applied research (such as drug developmen...

AI Technical Summary

Problems solved by technology

[0006] The technical problem to be solved by the present invention is to overcome the shortcomings of poor real-time performance and low accuracy caused by color deconvolution of microscopic images in the prior art through human intervention, and to provide a microscopic image based on non-negative matrix decomposition. The color deconvolution method and a microscopic image segmentation method based on non-negative matrix decomposition can effectively improve the accuracy of microscopic image segmentation, save computing time, and obtain better visual effects of microscopic images

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