Automatic fast segmenting method of tumor pathological image

Abstract

The invention discloses an automatic fast segmenting method of a tumor pathological image. The method comprises the following steps: firstly filtering a tumor original pathological image through the adoption of a Gaussian pyramid algorithm to respectively obtain pathological images with equal resolution, double resolution, fourfold resolution, eightfold resolution and 16-fold resolution; determining an initial region of interest containing the tumor on the equal resolution image through a RGB color model and morphological close operation; iteratively optimizing the initial regions of interest from the equal resolution to the fourfold resolution through the adoption of bhattacharyya distance; judging that the contribution of the RGB color model to the tumor region of interest has been reduced to zero when the bhattacharyya distance achieves a set threshold value; performing the self-adaptive high resolution selection of the deep precise segmentation through the adoption of a convergence exponent filtering algorithm, thereby further segmenting under the most suitable high resolution; and finally segmenting out a normal tissue and a tumor tissue in the tumor region of interest through the adoption of a bag of words model based on random projection. The method disclosed by the invention has the features of being accurate, fast and automatic.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a method for automatic and fast segmentation of tumor pathological images. Background technique [0002] Cancer has become one of the leading causes of death in developed countries. Taking colorectal cancer as an example, it is currently the third largest malignant tumor in the world. Due to the characteristics of full color and high image resolution, the current pathological section diagnosis has become one of the important means of tumor detection. However, at present, tumor diagnosis is completely dependent on the manual operation of pathologists, which is not only slow in efficiency, but also highly affected by the subjective judgment of doctors. Therefore, the computer-aided diagnosis system based on pathology is of great significance. Some studies have shown that the output results of pathology computer-aided diagnosis system can provide a reference fo...

AI Technical Summary

Problems solved by technology

These methods have some major disadvantages: (1) Most methods require manual interaction; (2) Most methods are sensitive to noise, and due to the low contrast of ultrasound images and tissue-related textures, accurate segmentation is difficult; (3) Most methods The complexity of the algorithm is high, the processing time is long, and it is difficult to meet the clinical requirements

However, these methods have the following problems: (1) The value of multi-resolution is fixed, often the robustness is not high, and it is only effective for some images, which affects the accuracy of automatic segmentation; (2) The current technical route of automatic segmentation and There are big differences in the artificial thinking of pathology doctors, and there are two kinds of results. One is that the algorithm is not suitable for pathological images, and the segmentation accuracy is not high; Speed ​​requirements for automatic segmentation

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