title: Medical image analysis with fuzzy models.
abstract: This paper updates several recent surveys on the use of fuzzy models for segmentation and edge detection in medical image data. Our survey is divided into methods based on supervised and unsupervised learning (that is, on whether there are or are not labelled data available for supervising the computations), and is organized first and foremost by groups (that we know of!) that are active in this area. Our review is aimed more towards 'who is doing it' rather than 'how good it is'. This is partially dictated by the fact that direct comparisons of supervised and unsupervised methods is somewhat akin to comparing apples and oranges. There is a further subdivision into methods for two- and three-dimensional data and/or problems. We do not cover methods based on neural-like networks or fuzzy reasoning systems. These topics are covered in a recently published companion survey by keller et al.
authors.affiliation: Department of Computer Science, University of West Florida, Pensacola 32514, USA. jbezdek@ai.uwf.edu
authors.forename: J C
authors.initials: JC
authors.lastname: Bezdek
authors.forename: L O
authors.initials: LO
authors.lastname: Hall
authors.forename: M C
authors.initials: MC
authors.lastname: Clark
authors.forename: D B
authors.initials: DB
authors.lastname: Goldgof
authors.forename: L P
authors.initials: LP
authors.lastname: Clarke
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D003952:Diagnostic Imaging; D017143:Fuzzy Logic; D006801:Humans; D007089:Image Enhancement / Q000379:methods*