title: Texture-based medical image retrieval in compressed domain using compressive sensing.
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.
authors.affiliation: Computer Science Department, College of Engineering Roorkee, Roorkee, India.
authors.forename: Kuldeep
authors.initials: K
authors.lastname: Yadav
authors.affiliation: Computer Science Department, College of Engineering Roorkee, Roorkee, India.
authors.forename: Avi
authors.initials: A
authors.lastname: Srivastava
authors.affiliation: Computer Science Department, Graphic Era University, Dehradun, India.
authors.forename: Ankush
authors.initials: A
authors.lastname: Mittal
authors.affiliation: Department of Electrical Engineering, School of Engineering, Gautam Buddha University (GBU), Greater Noida, India.
authors.forename: M A
authors.initials: MA
authors.lastname: Ansari
mesh_terms: D000465:Algorithms; D044962:Data Compression / Q000379:methods*; D016208:Databases, Factual; D003952:Diagnostic Imaging / Q000295:instrumentation / Q000379:methods*; D006801:Humans; D007089:Image Enhancement; D016247:Information Storage and Retrieval; D010363:Pattern Recognition, Automated / Q000379:methods; D013902:Radiography, Thoracic / Q000379:methods; D012984:Software
keywords: DCT; acquisition speed; basis pursuit algorithm; compressed domain image retrieval; compressive sampling; compressive sensing; content based image retrieval; discrete cosine transform; image quality; medical images; medical imaging; texture based image retrieval