T1 Weighted MR Brain Image Segmentation with Triangular Intuitionistic Fuzzy Set
Gagan Kumar Koduru1, Kuda Nageswararao2, Anupama Namburu3

1Gagan Kumar Koduru*, Department of Computer Science & Systems Engineering, Andhra University, Visakhapatnam, India.
2Kuda Nageswararao, Department of Computer Science & Systems Engineering, Andhra University, Visakhapatnam, India.
3Anupama Namburu, Department of CSE, Vellore Institute of Technology, Amaravati, India.
Manuscript received on January 11, 2020. | Revised Manuscript received on January 24, 2020. | Manuscript published on February 10, 2020. | PP: 762-768 | Volume-9 Issue-4, February 2020. | Retrieval Number: C8384019320/2020©BEIESP | DOI: 10.35940/ijitee.C8384.029420
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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: ISegmentation of medical image is a very important step in processing of image to help examination of diseases. The early detection of ailments from the normal diseases is essential for the physicist to stop and provide treatment. Increasing Cerebrospinal fluid in brain causes dementia which is an increasing mostly prevalent now days. Segmentation of brain images is a challenge due to the existence of noise and intensity in-homogeneity that creates hesitation in segmenting the tissues. This paper is about a fresh segmentation method that uses triangular membership function to distinguish the early regions of brain tissue with intuitionistic fuzzy set. The triangular membership function helps in identifying the initial clusters and regions and facilitates in the decline of the number of iterations needed for segmentation. The proposed method successfully determines the brain tissues avoiding local minima and need of initial clusters. Hence, outperforms the existing method with increased accuracy and reduced computation time 
Keywords: Intuitionistic Fuzzy Sets, Segmentation, Rough Sets, Brain Image, Soft Sets.
Scope of the Article:  Fuzzy logics