Open AccessParameterization of BFO Algorithm for the Improved Functionality of MFKM Technique for Better Pathological Identification in Brain MR Image
Author(s) -
Anitha Narayanan,
Yudong Zhang,
Pallikonda Rajasekaran Murugan,
Vishnuvarthanan Govindaraj,
Vigneshwaran Senthilvel,
Sindhuja Sankaran
Publication year - 2019
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.b1157.1292s219
Subject(s) - segmentation , algorithm , computer science , artificial intelligence , fuzzy logic , identification (biology) , noise (video) , image segmentation , computer vision , image (mathematics) , pattern recognition (psychology) , botany , biology
Intensity inhomogeneity, high level of noise, partial volume effect and poor image contrast are the major artefacts in medical image segmentation. Any of these artefacts might lead to unclear boundaries of tissues, hence the segmentation of tissues in the MR brain image cannot be determined with high accuracy, and this would be a problem to the radiologists to diagnose or to start the treatment because of the lack of facility to operate over the brain in in-vivo condition. This makes the radiologist and surgeons/experts to take time to come for the conclusion on pathology of a particular patient. So, the radiologists and experts need to give more exertion when this condition is applied for many patients at a day, to diagnose and to start treatment. To make this effortless to them, also for accurate diagnosis, this research paper provides an robust algorithm using the Modified Fuzzy K-Means (MFKM) and Bacteria Foraging Optimization (BFO) algorithm, which segments the abnormal tissues among the normal tissues from MR brain images with high accuracy. The accuracy of the Improved MFKM (IMFKM) algorithm is obtained in terms of Sensitivity and Specificity, and the proposed algorithm proves better segmentation results than the other conventional algorithms.