Texture-based medical image retrieval in compressed domain using compressive sensing.

Q4 Health Professions

International Journal of Bioinformatics Research and Applications Pub Date : 2014-01-01 DOI:10.1504/IJBRA.2014.059519

Kuldeep Yadav, Avi Srivastava, Ankush Mittal, M A Ansari

引用次数: 12

Abstract

Content-based image retrieval has gained considerable attention in today's scenario as a useful tool in many applications; texture is one of them. In this paper, we focus on texture-based image retrieval in compressed domain using compressive sensing with the help of DC coefficients. Medical imaging is one of the fields which have been affected most, as there had been huge size of image database and getting out the concerned image had been a daunting task. Considering this, in this paper we propose a new model of image retrieval process using compressive sampling, since it allows accurate recovery of image from far fewer samples of unknowns and it does not require a close relation of matching between sampling pattern and characteristic image structure with increase acquisition speed and enhanced image quality.

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基于压缩感知的压缩域医学图像纹理检索。

基于内容的图像检索作为一种有用的工具在当今的场景中得到了广泛的关注;纹理就是其中之一。本文主要研究基于DC系数的压缩感知压缩域纹理图像检索。医学成像是受影响最大的领域之一，因为图像数据库规模庞大，提取相关图像是一项艰巨的任务。考虑到这一点，本文提出了一种使用压缩采样的图像检索过程的新模型，因为它可以从更少的未知样本中准确地恢复图像，并且不需要采样模式与特征图像结构之间的紧密匹配关系，从而提高了采集速度和图像质量。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Bioinformatics Research and Applications Health Professions-Health Information Management

CiteScore

0.60

自引率

0.00%

发文量

期刊介绍： Bioinformatics is an interdisciplinary research field that combines biology, computer science, mathematics and statistics into a broad-based field that will have profound impacts on all fields of biology. The emphasis of IJBRA is on basic bioinformatics research methods, tool development, performance evaluation and their applications in biology. IJBRA addresses the most innovative developments, research issues and solutions in bioinformatics and computational biology and their applications. Topics covered include Databases, bio-grid, system biology Biomedical image processing, modelling and simulation Bio-ontology and data mining, DNA assembly, clustering, mapping Computational genomics/proteomics Silico technology: computational intelligence, high performance computing E-health, telemedicine Gene expression, microarrays, identification, annotation Genetic algorithms, fuzzy logic, neural networks, data visualisation Hidden Markov models, machine learning, support vector machines Molecular evolution, phylogeny, modelling, simulation, sequence analysis Parallel algorithms/architectures, computational structural biology Phylogeny reconstruction algorithms, physiome, protein structure prediction Sequence assembly, search, alignment Signalling/computational biomedical data engineering Simulated annealing, statistical analysis, stochastic grammars.