A medical automatic image processing system

Abstract

The invention discloses a medical automatic image processing system. The image data mining theoretical research is tightly combined with the practical application, and the mainstream technology of current medical image data mining is discussed; the method is based on improvement of a statistical learning theory, a decision tree classification algorithm, an association rule algorithm and a rough set decision tree algorithm. The invention further discloses an association rule classification method, so problems existing in association rule classification are solved; the format of the multi-sourcemedical equipment data is packaged, and a unified processing interface is adopted to form a normalized data directory, so the classifying efficiency is improved and the processing time is reduced; meanwhile, the key technology can be applied to computer-aided diagnosis through the construction of the medical knowledge graph, and the medical automatic image processing system based on the image data mining technology is constructed.

Description

technical field [0001] The present invention relates to the technical field of image recognition and knowledge map, in particular to a medical automatic image processing system. Background technique [0002] The data mining of medical images aims to mine effective models, associations, rules, changes, irregularities and general laws from massive image data, so as to speed up the process of doctors' decision-making and diagnosis and improve the accuracy of their decision-making and diagnosis. With the rapid development of medical digital equipment, medical information databases include not only structured information of patients, but also a large number of unstructured medical image information of patients, which provides a rich data source for data mining of medical images. [0003] As a method of deep retrieval and mining, knowledge graph is widely used in the accurate acquisition of big data information. In recent years, some research results have been achieved in the med...

AI Technical Summary

Problems solved by technology

These systems have difficulties in acquiring and expressing knowledge, and have a certain degree of subjectivity, and their intelligence and robustness are relatively poor.

Moreover, most of the existing medical knowledge graphs are constructed based on text-based unstructured cases and documents, rather than based on medical images, so it is impossible to mine and utilize image data.

Commonly used image segmentation technologies usually have problems such as difficult classification, poor correlation and contrast, and insufficient granularity of image recognition, so they are not suitable for medical image processing.

Images