Medical image classification method based on corner matching

Abstract

The present invention belongs to the healthcare data mining technology, especially to a medical image classification method based on corner matching. The method comprises: making a classification request for a medical image to be classified; extracting the sequence of vertex C of I; calculating an initial matching corner dot pair sequence set DS; calculating the initial matching corner dot pair sequence set DS; calculating the maximum matching corner dot pair sequence set DM; calculating the common k neighbor matching corner dot pair sequence set DS; calculating t medical images being the most similar to the I; and outputting the classification labels of the I according to the voting mechanism. The medical image classification method based on corner matching provides the definition of one-to-one maximum matching corner dot pair sequence, gives out problem that the solution of the one-to-one maximum matching corner dot pair sequence problem in the one-to-many matching corner dot pair sequence is converted to the solution of the bipartite graph maximum matching problem, employs the Hungary algorithm to perform solution, and provides a medical image similarity calculation formula based on the matching corner dot pair, wherein the formula considers the matching corner dot pair sequence and the non-matching corner dots so as to improve the accuracy of the corner dot matching and improve the accuracy of the classification result.

Description

technical field [0001] The invention belongs to the field of medical and health data mining, and in particular relates to a medical image classification method based on corner point matching. Background technique [0002] Medical images can well show the spatial structure of human organs, and at the same time can clearly display the disease information of the patient's body, providing a reliable basis for the doctor's diagnosis. The mature development of medical imaging technology has promoted the rapid growth of medical image data, which has also prompted the emergence of some systems for storing medical images, such as Picture Archiving and Communication Systems (PACS), Medical Digital Imaging and Communication (Digital Imaging and Communications in Medicine, DICOM), etc. These systems store a large amount of patient information, information on the development of the patient's condition, and a series of precious diagnoses made by doctors to patients. By finding medical i...

AI Technical Summary

Problems solved by technology

There are following deficiencies in this method: (1) efficiency is low

Coarse-grained matching only uses the movable range properties of corner points, but does not use the spatial relationship between corner points and other corner points, so the establishment of KAP graph in this process increases the execution time and complexity of the algorithm

(2) Some important matching vertex pairs are lost

This is because during the coarse-grained matching process, if the vertex pair has one-to-many situation, the nearest vertex allocation strategy adopted by this method leads to the loss of some important matching vertex pairs

(3) The similarity between the image to be classified and the template image only considers matching vertex pairs, while ignoring the unmatched vertices in the two images

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