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

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 im...

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Vydané v:International journal of bioinformatics research and applications Ročník 10; číslo 2; s. 129
Hlavní autori: Yadav, Kuldeep, Srivastava, Avi, Mittal, Ankush, Ansari, M A
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Switzerland 2014
Predmet:
Algorithms
Data Compression - methods
Databases, Factual
Diagnostic Imaging - instrumentation
Diagnostic Imaging - methods
Humans
Image Enhancement
Information Storage and Retrieval
Pattern Recognition, Automated - methods
Radiography, Thoracic - methods
Software
medical images
basis pursuit algorithm
compressed domain image retrieval
discrete cosine transform
image quality
DCT
texture based image retrieval
acquisition speed
compressive sampling
content based image retrieval
compressive sensing
medical imaging
ISSN:1744-5485
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Shrnutí: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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1744-5485
DOI:10.1504/IJBRA.2014.059519