A Medical Image Retrieval Method Based on Deep Learning and Radon Transform

A medical image and deep learning technology, applied in the fields of computer vision and image retrieval, can solve problems such as high complexity, and achieve the effect of reducing the dimension of feature vectors, improving retrieval accuracy, and improving accuracy

Active Publication Date: 2019-08-09
BEIJING UNIV OF TECH
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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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  • A Medical Image Retrieval Method Based on Deep Learning and Radon Transform
  • A Medical Image Retrieval Method Based on Deep Learning and Radon Transform
  • A Medical Image Retrieval Method Based on Deep Learning and Radon Transform

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[0052] In order to make the objectives, technical solutions, and related advantages of the present invention clearer and clearer, the present invention will be further described in detail below in conjunction with the drawings and specific embodiments.

[0053] The present invention proposes a medical image retrieval method based on deep learning and Radon transform. The method uses BING target suggestion algorithm to block the input image and then inputs the constructed deep convolutional network, and at the same time introduces partial mean Pooling in the network Extracting distinguishing features, in the similarity measurement process, introducing a product quantization algorithm, effectively reducing the computational complexity, and obtaining "coarse" retrieval results. On this basis, the Radon transformation method is used to make the "coarse" retrieval results more accurate Retrieval, and finally get the Top10 image most similar to the query image. The key steps of the met...

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Abstract

A medical image retrieval method based on deep learning and Radon transform involves the fields of computer vision and image retrieval. In the "coarse" retrieval stage, the BING target suggestion algorithm is used to detect regions with significant objects. By introducing partial mean pooling into the deep convolutional network architecture, significant distinguishing features based on regions can be extracted and feature dimensions can be reduced, and then aggregated to form A global feature representation. 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. In the "fine" retrieval stage, with the help of Radon transformation, the image can be integrally projected from multiple angles to obtain the characteristics of more detailed information of the image. The Top50 images obtained in the "coarse" retrieval are generated through Radon transformation to generate a Radon barcode, and the similarity measurement is achieved. Search more precisely. The invention improves the accuracy rate of medical image retrieval, and overcomes the problem of low feature distinction and high feature dimension of medical image retrieval caused by direct use of convolutional neural network.

Description

Technical field [0001] The invention relates to the field of computer vision and image retrieval, in particular to a medical image retrieval method based on deep learning and Radon transformation. 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 that doctors need from them is an important topic in medical image research today. The traditional content-based medical image retrieval (CBMIR) method is to sequentially compare the query image with the image in the database one by one. Its linear complexity leads to its shortcomings such as low efficiency and low scalability in the real environment, while extracting features It is a low-lev...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F16/53G06K9/66G06N3/08
CPCG06F16/583G06N3/08G06V30/194
Inventor 蔡轶珩邱长炎高旭蓉崔益泽王雪艳孔欣然
Owner BEIJING UNIV OF TECH
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