Tissue pathology image recognition method

Abstract

The invention discloses a tissue pathology image recognition method. The method comprises the following steps of choosing disease-free and diseased training samples and disease-free and diseased testing samples; combining the disease-free training sample and the diseased training sample, establishing a disease-free dictionary study model and a diseased dictionary study model, alternately iterating and optimizing two target functions till reaching maximum iteration frequency, and obtaining a disease-free dictionary and a diseased dictionary through study; utilizing the disease-free dictionary and the diseased dictionary, conducting sparse representation on the testing samples, and calculating a sparse reconstruction error vector of the testing samples under the disease-free dictionary and the diseased dictionary; obtaining a classification statistic through the sparse reconstruction error vector, and comparing the classification statistic with a threshold value to obtain the classification of the testing samples. According to the tissue pathology image recognition method, a new model and method is provided for dictionary study in a tissue pathology image classification, and the studied dictionary with type class has good sparse reconfigurability and intra-class robustness for similar samples, and has good class inter-class discrimination for non-similar samples.

Description

technical field [0001] The invention relates to a histopathological image recognition method. Background technique [0002] With the development of computer-aided diagnosis technology, the research of "digital pathology" has gradually attracted the attention of the majority of scientific researchers. Among them, how to accurately and automatically extract the discriminative features hidden in the image can provide a basis for subsequent histopathological image analysis or classification. The necessary information to quickly and accurately give disease grades and classifications has become one of the most challenging research topics in "digital pathology". [0003] Traditional feature extraction methods are mainly divided into the following two categories: the first category is based on specific domain or task-specific features, such as the size and shape of biological cells, grayscale or color information of images, texture, etc.; the second category Mainly focus on spatial...

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Problems solved by technology

[0006] However, due to the different characteristics presented by different types of histopathological images, the characteristics of cell morphology and geometric structure in the same type of histopathological images vary greatly, and the pathological features also show diversification, which leads to feature differences among samples of the same pathological image. It is larger than the feature difference between non-similar pathological image samples, so that the diseased dictionary learned by the above method is more similar to the disease-free dictionary, and the discrimination between the disease-free sample and the diseased sample is still low, and its classification performance is still to be determined. to improve

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