Pathological image diagnosis of cervical cancer based on poisson 's ring conditional random field

Abstract

The invention relates to a cervical cancer tissue pathological image diagnosis method based on a Poisson ring conditional random field. The method comprises the following steps: 101, acquiring digitalcervical cancer histopathological image; 102, preprocessing the obtained digital histopathological images of cervical cancer; 103, clustering, segmenting and blocking the preprocessed digital cervical cancer histopathological images by using a segmentation algorithm, and obtaining a plurality of image blocks; 104, extracting features from each image block obtained in step 103, and then performingfeature selection on the extracted features; 105. The histopathological images of cervical cancer are graded by conditional random field model, and the graded results of histopathological images areobtained. The invention provides a cervical cancer tissue pathological image diagnosis method based on a Poisson ring conditional random field, which can obtain the grading result of cervical cancer according to the cervical cancer tissue pathological image.

Description

technical field [0001] The invention belongs to the technical field of cervical cancer histopathological image diagnosis, and in particular relates to a method for diagnosing cervical cancer histopathological images based on Poisson ring condition random field. Background technique [0002] 1.1 Brief description of the scheme of the prior art [0003] Existing techniques use conditional random fields to classify cervical colposcopy images, with histopathology images as a ground truth. Such as figure 1 As shown, the method consists of five steps from top to bottom: [0004] (1) Preprocessing includes three parts: image calibration, image registration and anatomical feature extraction; [0005] (2) Image segmentation uses a k-means-based clustering method to identify subregions in each tissue type that are homogeneous in color and intensity; [0006] (3) Diagnostically relevant features including, but not limited to, acetic acid blanching, mosaicism, punctate, and atypical...

AI Technical Summary

Problems solved by technology

[0009] 1.2 Objective Shortcomings of the Prior Art

[0010] (1) In the existing technology, the histopathological images of cervical cancer are only used as basic facts, and experienced pathologists are still required to make judgments on the images. There are also differences in judgment, which may produce large errors

[0011] (2) Existing technology still requires experienced pathologists to make judgments, but the number of pathologists is limited, and medical resources are lacking in underdeveloped areas, and pathologists are also scarce; medical students or pathologists with insufficient experience , cannot make reliable judgments on histopathological images

[0012] (3) The existing technology can only use a conditional random field-based classifier to judge whether a local area is abnormal, and cannot give the classification results of cervical cancer, such as precancerous lesions (CINI, CINII, CINIII grade 3) and malignant tumors (Three grades of high, middle and low differentiation)

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