title: Handcrafted fuzzy rules for tissue classification.
abstract: This article proposes a handcrafted fuzzy rule-based system for segmentation and identification of different tissue types in magnetic resonance (MR) brain images. The proposed fuzzy system uses a combination of histogram and spatial neighborhood-based features. The intensity variation from one type of tissue to another is gradual at the boundaries due to the inherent nature of the MR signal (MR physics). A fuzzy rule-based approach is expected to better handle these variations and variability in features corresponding to different types of tissues. The proposed segmentation is tested to classify the pixels of the T2-weighted axial MR images of the brain into three primary tissue types: white matter, gray matter and cerebral-spinal fluid. The results are compared with those from manual segmentation by an expert, demonstrating good agreement between them.
authors.affiliation: Philips Innovation Campus, Nagavara, Bangalore 560045, India. sbm20@yahoo.com
authors.forename: Shashi Bhushan
authors.initials: SB
authors.lastname: Mehta
authors.forename: Santanu
authors.initials: S
authors.lastname: Chaudhury
authors.forename: Asok
authors.initials: A
authors.lastname: Bhattacharyya
authors.forename: Amarnath
authors.initials: A
authors.lastname: Jena
mesh_terms: D001921:Brain / Q000033:anatomy & histology*; D017143:Fuzzy Logic; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging; D010363:Pattern Recognition, Automated