Medical image retrieval method based on deep learning and Radon conversion

Abstract

The invention discloses a medical image retrieval method based on deep learning and Radon conversion, and relates to the field of computer vision and image retrieval. In the crude retrieval stage, a BING target suggestion algorithm is adopted for detecting a region with a remarkable object, a partial mean value Pooling is introduced into deep convolution network architecture, region-based remarkable differentiation characteristics can be extracted, the characteristic dimensions are reduced, and then polymerization is performed to form global characteristic expression. In the characteristic vector quantization process, and a product quantization algorithm is used for solving the problem that characteristic vector similarity measurement calculation is high in complexity. In the fine retrieval stage, Radon conversion is used for performing integral projection on the image at multiple angles, more detailed information characteristics of the image can be obtained, and the Top 50 images obtained in the crude retrieval are subjected to Radon conversion to generate Radon bar codes, and more accurate retrieval is achieved through similarity measurement. The accuracy of the medical image retrieval is improved, and the medical image retrieval problems that the characteristic differentiation is not high and the characteristic dimension is high due to direct use of a convolution neural network are solved.

Description

technical field [0001] The invention relates to the fields of computer vision and image retrieval, in particular to a medical image retrieval method based on deep learning and Radon transform. Background technique [0002] With the increasing application of image processing technology in the medical field, a large number of medical images are generated every day, such as CT images, B-ultrasound images, MRI images, etc., which are an important basis for clinical diagnosis and treatment and medical image research. How to effectively manage these medical images and retrieve the information needed by doctors is an important topic in the research of medical images today. The traditional content-based medical image retrieval (CBMIR) method is to sequentially compare the query image with the images in the database one by one, and its linear complexity leads to its disadvantages such as low efficiency and low scalability in the real environment, while extracting features It is a lo...

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Problems solved by technology

At the same time, in the process of eigenvector quantization, the product quantization algorithm is used to solve the problem of high computational complexity of the similarity measure between eigenvectors

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