Please use this identifier to cite or link to this item: http://idr.nitk.ac.in/jspui/handle/123456789/7388
Title: Artificial Bee Colony (ABC) based variable density sampling scheme for CS-MRI
Authors: Jagadish, A.K.
Goswami, S.
Saha, P.
Chakrabarty, S.
Rajgopal, K.
Issue Date: 2017
Citation: IEEE Region 10 Annual International Conference, Proceedings/TENCON, 2017, Vol., , pp.1254-1257
Abstract: The self-sustained dynamics of the bee population in nature is a result of their hierarchical working culture, efficient organizing skills and unique highly developed foraging ability, which enables them to interact effectively among each other as well as with their environment. In this paper, a novel algorithm utilizing the bee's swarm intelligence, and its heuristics based on quality and quantity of food sources (nectars) is proposed to generate a variable density sampling (VDS) scheme for compressive sampling (CS) based fast MRI data acquisition. The algorithm uses the scout-bees for global random selection process which is further fine-tuned by employed and onlooker-bees who forage locally in the neighborhood giving prime importance to points possessing high fitness values (or high energy) usually located around the center of fc-space. The algorithm introduces the concept of searching for the high quality food sources in annular regions, called as bins, of varying widths. Retrospective CS-MRI simulations show that the proposed fc-ABC based VDS scheme performs significantly better than other sampling schemes. � 2016 IEEE.
URI: http://idr.nitk.ac.in/jspui/handle/123456789/7388
Appears in Collections:2. Conference Papers

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