Medical CT image storage and retrieval method based on random forest hash

Abstract

The invention claims a medical CT image efficient storage and retrieval method based on random forest hash learning. According to the method, a random forest hash model is trained with a medical CT image set, and the model and a hash code library corresponding to an image library are stored. When a user enters a new image that needs to be retrieved, the model maps the image to a hash code; K hashcodes nearest to the hash code are retrieved in the hash code library; and the K hash codes are decoded and reconstructed into an image through a maximum compatible rule defined by the decision pathsof a tree, and the image is returned to the user. With the method implemented, the retrieval speed of images can be effectively improved, and the storage space of the images is greatly saved; the medical diagnosis of doctors can be facilitated; the workload of the doctors can be reduced; work efficiency can be improved; and the utilization rate of hospital storage devices can be improved.

Description

technical field [0001] The invention belongs to the field of medical image processing, and in particular relates to medical CT image storage and retrieval method technology. Background technique [0002] In the era of big data, the amount of data has increased significantly, and the infrastructure of modern information technology must be able to handle huge data, resulting in increased storage, transmission, and management costs. In fact, searching for relevant content in large databases becomes more challenging than these costs. Especially searching media data such as audio, image and video remains a major challenge. Besides the widely used commercial text-based search engines such as Google, Baidu, and Bing, content-based image retrieval has attracted extensive attention in the past decade. Content-based image retrieval does not need to rely on text-based keyword-based index structures, and directly and efficiently indexes media content to directly respond to visual quer...

AI Technical Summary

Problems solved by technology

[0005] However, when the existing hash learning technology is applied to image retrieval, the original image library needs to be saved, and the retrieval process still needs to utilize the original image library, which does not completely reduce the data storage space.

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