Medical big data retrieval method based on visual vocabulary and multi-feature matching Skyline

Abstract

A medical big data retrieval method based on visual vocabulary and multi-feature matching Skyline belongs to the crossing field of intelligent medical and big data processing. According to the system, metric space Skyline query is applied to the medical image retrieval technology of basic content. The technical points comprise: feature data such as SIFT and Color are extracted; multiple bottom layer features of an image are fused by means of distributed Skyline operation; each feature similarity is used as an assessment target of the Skyline; return results are candidate images which are similar to the query image in multi-dimensional features or extremely similar to the query image in a certain dimension feature; finally, streaming processing is carried out by means of a cloud calculation Spark system to obtain query or processing results in real time. The method has following beneficial effects: obtaining information corresponding to an image from a user terminal and uploading and saving the information to a cloud server; then the cloud server carries out processing to obtain optimal medical image cluster scheme and feed back to users.

Description

technical field [0001] The patent of the present invention belongs to the intersection field of smart medical care and large number processing. It is a Skyline medical big data retrieval system based on visual vocabulary and multi-feature matching. The system applies metric space Skyline query to content-based medical image retrieval technology. , involving large-scale medical data analysis, massive data processing in the cloud computing environment, and intelligent data processing and application development. Background technique [0002] With the development of the Internet and the popularization of medical digital equipment, medical image data has grown exponentially, and the retrieval technology of related image data has attracted more and more attention. Massive data not only has the characteristics of large data volume, but also contains Huge commercial value. For example, analyzing the tumor growth of medical cancer users can guide doctors to recommend relevant perso...

AI Technical Summary

Problems solved by technology

In the semantic image retrieval method with medical background, although the semantic information of the image is rich, there are also shortcomings such as complex semantic information, subjective semantic understanding, semantic extraction and expression difficulties, which affect the metric space modeling and medical image retrieval. In addition, due to the ambiguity of semantic information, most algorithms select multiple images to participate in the query according to semantic needs in order to improve the query accuracy, which greatly increases the amount of calculation in the query process

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