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Parallel Implementation of Texture based Medical Image Retrieval in Compressed Domain using CUDA

Novel Aspects of Digital Imaging Applications
© 2011 by IJCA Journal
ISBN: 978-93-80865-47-9
Year of Publication: 2011
Kuldeep Yadav
Avi Srivastava
M.A Ansari

Kuldeep Yadav, Avi Srivastava and M A Ansari. Parallel Implementation of Texture based Medical Image Retrieval in Compressed Domain using CUDA. IJCA Special Issue on Novel Aspects of Digital Imaging Applications (DIA) (1):53–58, 2011. Full text available. BibTeX

	author = {Kuldeep Yadav and Avi Srivastava and M.A Ansari},
	title = {Parallel Implementation of Texture based Medical Image Retrieval in Compressed Domain using CUDA},
	journal = {IJCA Special Issue on Novel Aspects of Digital Imaging Applications (DIA)},
	year = {2011},
	number = {1},
	pages = {53--58},
	note = {Full text available}


In huge databases, Image processing takes more time for execution on a single core processor because of slow single thread algorithms. Graphics Processing Unit (GPU) is more popular now-a-days due to their speed, programmability, low cost and more inbuilt execution cores in it. Most of the researchers started work to use GPUs as a processing unit with a single core computer system to speedup execution of algorithms. The main goal of this research work is to parallelize the process of content based image retrieval through texture and that to in compressed domain making whole process much faster than normal. In this paper, parallel implementation is focused on the well known Euclidean Distance approach for texture based image retrieval systems, since it is one of the most fundamental and important problems in the field of computer vision and content based image retrieval (CBIR) and for compressed images we have taken standard JPEG format. Our work employs extensive usage of highly multithreaded architecture of multi-cored GPU. An efficient use of shared memory is required to optimize parallel reduction in Compute Unified Device Architecture (CUDA). Experimental results show that parallel implementation achieved an average speed up of 30 x over the serial implementation when running on a GPU named GeForce 9500 GT having 32 cores. Texture based retrieval method of CBIR is also evaluated using Recall, Precision, F-measure, True Negative rate, and Accuracy evaluation measures.


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