title: Spatial Fuzzy C Means and Expectation Maximization Algorithms with Bias Correction for Segmentation of MR Brain Images.
abstract: The Fuzzy C Means (FCM) and Expectation Maximization (EM) algorithms are the most prevalent methods for automatic segmentation of MR brain images into three classes Gray Matter (GM), White Matter (WM) and Cerebrospinal Fluid (CSF). The major difficulties associated with these conventional methods for MR brain image segmentation are the Intensity Non-uniformity (INU) and noise. In this paper, EM and FCM with spatial information and bias correction are proposed to overcome these effects. The spatial information is incorporated by convolving the posterior probability during E-Step of the EM algorithm with mean filter. Also, a method of pixel re-labeling is included to improve the segmentation accuracy. The proposed method is validated by extensive experiments on both simulated and real brain images from standard database. Quantitative and qualitative results depict that the method is superior to the conventional methods by around 25% and over the state-of-the art method by 8%.
authors.affiliation: Department of Electronics and Communication Engineering, P. S. R. Engineering College, Sivakasi, India. meenaprakash73@gmail.com.
authors.forename: R
authors.initials: R
authors.lastname: Meena Prakash
authors.affiliation: Department of Electronics and Communication Engineering, Mepco Schlenk Engineering College, Sivakasi, India.
authors.forename: R
authors.initials: R
authors.lastname: Shantha Selva Kumari
mesh_terms: D000465:Algorithms; D001921:Brain / Q000000981:diagnostic imaging*; D017143:Fuzzy Logic; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*
keywords: Expectation maximization; Fuzzy C means; Gaussian mixture model; MR brain image segmentation