Intuitionistic fuzzy roughness measure for segmentation of brain MR images

Author

Dubey, Yogita K. ; Mushrif, Milind M.

Author_Institution

Dept. of Electron. & Telecommun., Yeshwantrao Chavan Coll. of Eng., Nagpur, India

fYear

2015

fDate

4-7 Jan. 2015

Firstpage

1

Lastpage

6

Abstract

A multilevel thresholding method for the segmentation of Magnetic Resonance (MR) brain images using the concept of intuitionistic fuzzy and rough set is presented here. Intuitionistic fuzzy roughness measure, calculated by considering histogram as lower approximation of rough set and intuitionistic fuzzy histon as upper approximation of rough set, is used to find optimum valley points for segmentation of brain MR images. A new fuzzy complement function is proposed for intuitionistic fuzzy image representation to take into account intensity inhomogeneity and noise in brain MR images. The proposed algorithm segments brain MR image into three regions, gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF). The quantitative evaluation demonstrate the superiority of the proposed algorithm.

Keywords

brain; fuzzy set theory; image segmentation; magnetic resonance imaging; medical image processing; rough set theory; CSF; brain MR image segmentation; cerebrospinal fluid; gray matter; intuitionistic fuzzy histon; intuitionistic fuzzy image representation; intuitionistic fuzzy roughness measure; magnetic resonance brain image segmentation; multilevel thresholding method; optimum valley point; rough set; white matter; Approximation methods; Brain; Histograms; Image segmentation; Noise; Noise level; Nonhomogeneous media; brain MR image; intuitionistic fuzzy set; rough set; roughness; segmentation;

fLanguage

English

Publisher

ieee

Conference_Titel

Advances in Pattern Recognition (ICAPR), 2015 Eighth International Conference on

Conference_Location

Kolkata

Type

conf

DOI

10.1109/ICAPR.2015.7050657

Filename

7050657