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A Content-Based medical image retrieval Method Using Relative Difference-Based Similarity Measure

Intelligent Automation & Soft Computing 2023年第8期37卷 2355-2370页

作者： Ali Ahmed Alaa Omran Almagrabi Omar MBarukab Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz University–RabighRabigh21589Saudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Information Technology Faculty of Computing and Information TechnologyKing Abdulaziz University-RabighRabigh21589Saudi Arabia

Content-based medical image retrieval(CBMIR)is a technique for retrieving medical images based on automatically derived image *** are many applications of CBMIR,such as teaching,research,diagnosis and electronic patient *** methods are applied to enhance the retrieval performance of CBMIR *** new and effective similarity measure and features fusion methods are two of the most powerful and effective strategies for improving these *** study proposes the relative difference-based similarity measure(RDBSM)for *** new measure was first used in the similarity calculation stage for the CBMIR using an unweighted fusion method of traditional color and texture ***,the study also proposes a weighted fusion method for medical image features extracted using pre-trained convolutional neural networks(CNNs)*** proposed RDBSM has outperformed the standard well-known similarity and distance measures using two popular medical image datasets,Kvasir and PH2,in terms of recall and precision retrieval *** effectiveness and quality of our proposed similarity measure are also proved using a significant test and statistical confidence bound.

关键词： medical image retrieval feature extraction similarity measure fusion method

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medical image retrieval Based on Multi-Layer Resampling Template

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Computer Aided Drafting,Design and Manufacturing 2014年第4期24卷 69-73页

作者： WANG Xin-rui YANG Yun-feng School of Mathematics and Statistics Northeast Petroleum University

medical image application in clinical diagnosis and treatment is becoming more and more widely, How to use a large number of images in the image management system and it is a very important issue how to assist doctors to analyze and diagnose. This paper studies the medical image retrieval based on multi-layer resampling template under the thought of the wavelet decomposition, the image retrieval method consists of two retrieval process which is coarse and fine retrieval. Coarse retrieval process is the medical image retrieval process based on the image contour features. Fine retrieval process is the medical image retrieval process based on multi-layer resampling template, a multi-layer sampling operator is employed to extract image resampling images each layer, then these resampling images are retrieved step by step to finish the process from coarse to fine retrieval.

关键词： medical image retrieval resampling mutual information

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A Fast image retrieval Algorithm with Multi-Channel Textural Features in PACS

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Wuhan University Journal of Natural Sciences 2005年第5期10卷 847-850页

作者： ZHANG Dong YANG Yan QIN Qian-qing School of Physics and Technology Wuhan University Wuhan 430072 Hubei China School of Electronic Information Wuhan University Wuhan 430072 Hubei China State Key Laboratory of Cartographical Remote Sensing and Information Engineering Wuhan University Wuhan 430079 Hubei China

The paper presents a fast algorithm for image retrieval using multi-channel textural features in medical picture archiving and communication system （PACS）. By choosing different linear or nonlinear operators in prediction and update lifting step, the linear or nonlinear M-band wavelet decomposition can be achieved in M-band lifting. It provides the advantages such as fast transform, in-place calculation and integer-integer transform. The set of wavelet moment forms multi-channel textural feature vector related to the texture distribution of each wavelet images. The experimental results of CT image database show that the retrieval approach of multi-channel textural features is effective for image indexing and has lower computational complexity and less memory. It is much easier to implement in hardware and suitable for the applications of real time medical processing system.

关键词： integer wavelet decomposition multi-channel textural feature medical image retrieval

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