International Journal of Computer Applications |
Foundation of Computer Science (FCS), NY, USA |
Volume 117 - Number 13 |
Year of Publication: 2015 |
Authors: Ankita Mitra, Arunava De, Anup Kumar Bhattacharjee |
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Ankita Mitra, Arunava De, Anup Kumar Bhattacharjee . Integration of Entropy Maximization and Quantum Behaved Particle Swarm Algorithm for Unsupervised Change Detection of MR Skull Bone Lesions. International Journal of Computer Applications. 117, 13 ( May 2015), 33-39. DOI=10.5120/20617-3321
Entropy is the measure of randomness in a system whereas the entropy maximization procedure leads to the most probable state of a system behaviour. Entropy maximization using an optimization algorithm is used to find the threshold of the MR image of the brain. Standard Particle Swarm algorithm sufferes from stagnation. An automatic regrouping mechanism is used to deal with the stagnation. An Quantum Particle Swarm algorithm together with Entropy maximization helps us to get the most probable threshold value which correctly segments the lesions from the background in MR of brain. Using change detection algorithm the segmented object of the MR at time tx is compared with another object of the MR at the time ty . The proposed method is applied on variety of MR images having lesions and gives favourable results in identifying changes taking place in the human brain.