A method of arranging data for thoracic surgery

Abstract

The present invention relates to the field of medical data, in particular to a method for collating thoracic surgery examination data, comprising: performing K-SVD decomposition on a preset number of chest images without and with disease, to obtain normal dictionaries, normal sparse image sets, Abnormal dictionary, abnormal sparse image collection, and then obtain the heat value of each dictionary vector in the normal dictionary and abnormal dictionary, obtain the normal dictionary vector set and abnormal dictionary vector set according to the heat value of the normal dictionary, abnormal dictionary, and dictionary vector, and combine all chests K-SVD decomposition is performed on the slice image, and the final dictionary and the final sparse image set are obtained according to the normal dictionary vector set, the abnormal dictionary vector set and the heat value of each dictionary vector, and the final dictionary and the final sparse image set are stored and classified. The invention facilitates reading, writing and storage of the chest radiograph images after sorting, and quick query of relevant data features, enables doctors to quickly analyze the disease, and increases the efficiency of hospital admissions.

Description

technical field [0001] The invention relates to the field of medical data, in particular to a method for sorting out thoracic surgery examination data. Background technique [0002] When a hospital performs a physical examination on a patient or a doctor detects a patient's chest disease and respiratory disease, the patient's chest X-ray is usually obtained. With the hospital's long-term consultation, there will be a large number of chest X-ray images in the hospital database. These chest X-ray images are of great significance for the follow-up of patients and the big data analysis and mining of medical data. Usually, a large number of chest X-ray images need to be queried, read and stored to obtain the desired chest X-ray data characteristics. However, due to the large amount of chest X-ray data and the lack of data sorting and analysis, the query, reading and storage are efficient. Low, especially when it is necessary to acquire and analyze certain data features in a larg...

AI Technical Summary

Problems solved by technology

Usually, it is necessary to query, read and store a large number of chest radiograph images to obtain the desired chest radiograph data features. However, due to the large amount of chest radiograph data and the lack of data collation and analysis, the efficiency of query, reading and storage Low, especially when it is necessary to obtain and analyze certain data features in a large number of chest radiographs, such as analyzing the situation of different periods of pulmonary effusion, the acquisition efficiency of these data features will be lower, so that doctors can obtain the desired data Features are like looking for a needle in a haystack

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