title: Segmentation of brain MRI using SOM-FCM-based method and 3D statistical descriptors.
abstract: Current medical imaging systems provide excellent spatial resolution, high tissue contrast, and up to 65535 intensity levels. Thus, image processing techniques which aim to exploit the information contained in the images are necessary for using these images in computer-aided diagnosis (CAD) systems. Image segmentation may be defined as the process of parcelling the image to delimit different neuroanatomical tissues present on the brain. In this paper we propose a segmentation technique using 3D statistical features extracted from the volume image. In addition, the presented method is based on unsupervised vector quantization and fuzzy clustering techniques and does not use any a priori information. The resulting fuzzy segmentation method addresses the problem of partial volume effect (PVE) and has been assessed using real brain images from the Internet Brain Image Repository (IBSR).
authors.affiliation: Communications Engineering Department, University of Malaga, 29004 Malaga, Spain.
authors.forename: Andrés
authors.initials: A
authors.lastname: Ortiz
authors.forename: Antonio A
authors.initials: AA
authors.lastname: Palacio
authors.forename: Juan M
authors.initials: JM
authors.lastname: Górriz
authors.forename: Javier
authors.initials: J
authors.lastname: Ramírez
authors.forename: Diego
authors.initials: D
authors.lastname: Salas-González
mesh_terms: D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology* / Q000473:pathology; D016000:Cluster Analysis; D019295:Computational Biology; D003198:Computer Simulation; D016208:Databases, Factual / Q000706:statistics & numerical data; D003936:Diagnosis, Computer-Assisted / Q000706:statistics & numerical data; D017143:Fuzzy Logic; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000706:statistics & numerical data*; D008279:Magnetic Resonance Imaging / Q000706:statistics & numerical data*; D008959:Models, Neurological