Brain Tumor Detection using PSO-FCM Based Segmentation
Roshan Lal1, Santar Pal Singh2, Aditya Garg3, Poorvika S. Negi4

1Roshan Lal, Dept. of Comp. Sc.& Engg., ASET, Amity University, Noida, India.
2Santar Pal Singh*, School of Comp. Sc. & Engg., Galgotias University, Greater Noida, India.
3Aditya Garg, Dept. of Comp. Sc.& Engg., ASET, Amity University, Noida, India.
4Poorvika S. Negi, Dept. of Comp. Sc.& Engg., ASET, Amity University, Noida, India.
Manuscript received on September 23, 2019. | Revised Manuscript received on October 15, 2019. | Manuscript published on October 30, 2019. | PP: 4693-4696 | Volume-9 Issue-1, October 2019 | Retrieval Number: A1903109119/2019©BEIESP | DOI: 10.35940/ijeat.A1903.109119
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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: Brain tumors are a serious threat to one’s health and can form in any healthy human being. So, it’s necessary to have accurate diagnostic technique for the same, thus came CAT scans, and the Magnetic Resonance Imaging or better known as MRI. All these techniques at their core require advanced image processing techniques to accurately analyze the images for tumor threats. In this paper we are going to be looking at a subsection of image processing called segmentation. Segmentation plays a vital role in image processing as it is through this method that we acquire the Region of Interest (ROI) or in simpler terms the probabilistic area for tumor occurrence. We are proposing the use of hybrid technique that utilizes a combination of two algorithms, first is the Particle Swarm Optimization (PSO) for the selection of an initial particle and the other is Fuzzy C- Mean Segmentation which will create the actual segmentations.
Keywords: Fuzzy C-Mean, Image Processing, MRI, PSO, Region of Interest, Segmentation..