Parameterization of BFO Algorithm for the Improved Functionality of MFKM Technique for Better Pathological Identification in Brain MR Image
Anitha Narayanan1, Yudong Zhang2, Pallikonda Rajasekaran Murugan3, Vishnuvarthanan Govindaraj4, Vigneshwaran Senthilvel5, Sakthivel Sankaran6
1Anitha Narayanan, Department of ECE, Kalasalingam Academy of Research and Education Kalasalingam University, Srivilliputtur (Tamil Nadu), India.
2Yudong Zhang, Professor, F26 Informatics Building, Department of Informatics, University of Leicester, University Road, Leicester, LE1 7RH, UK.
3Pallikonda Rajasekaran Murugan, Department of ECE, Kalasalingam Academy of Research and Education Kalasalingam University, Srivilliputtur (Tamil Nadu), India.
4Vishnuvarthanan Govindaraj, Department of BME, Kalasalingam Academy of Research and Education Kalasalingam University, Srivilliputtur (Tamil Nadu), India.
5Vigneshwaran Senthilvel, Department of ECE, Kalasalingam Academy of Research and Education Kalasalingam University, Srivilliputtur (Tamil Nadu), India.
6Sakthivel Sankaran, Department of BME, Kalasalingam Academy of Research and Education Kalasalingam University, Srivilliputtur (Tamil Nadu), India.
Manuscript received on 10 December 2019 | Revised Manuscript received on 22 December 2019 | Manuscript Published on 30 December 2019 | PP: 956-961 | Volume-9 Issue-2S2 December 2019 | Retrieval Number: B11571292S219/2019©BEIESP | DOI: 10.35940/ijitee.B1157.1292S219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: Bacteria Foraging Optimization, Magnetic Resonance (MR) Image Segmentation, Modified Fuzzy K – Means, Tissue Segmentation, Tumor Identification.
Scope of the Article: Image Security