title: Segmentation of multivariate medical images via unsupervised clustering with "adaptive resolution".
abstract: The need for quantitative information is becoming increasingly important in the clinical field. In this paper we present an interactive X11 based system, devoted to segmentation of multivariate medical images, including an unsupervised neural network approach to clustering. The following steps are considered in the analysis sequence: feature extraction, reduction of dimensionality, unsupervised data clustering, voxel classification, interactive post-processing refinement. The environment turns out to be extremely interactive, thus making the user able to display and modify data during processing, to set parameters, to choose different methods and different tools for each step, and to define online the whole analysis sequence.
authors.affiliation: IST National Institute for Cancer Research, Genoa, Italy.
authors.forename: A
authors.initials: A
authors.lastname: Schenone
authors.forename: F
authors.initials: F
authors.lastname: Firenze
authors.forename: F
authors.initials: F
authors.lastname: Acquarone
authors.forename: M
authors.initials: M
authors.lastname: Gambaro
authors.forename: F
authors.initials: F
authors.lastname: Masulli
authors.forename: L
authors.initials: L
authors.lastname: Andreucci
mesh_terms: D000465:Algorithms; D001921:Brain / Q000000981:diagnostic imaging*; D016000:Cluster Analysis; D003196:Computer Graphics; D003952:Diagnostic Imaging; D017143:Fuzzy Logic; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D015999:Multivariate Analysis; D016571:Neural Networks, Computer; D019047:Phantoms, Imaging; D011859:Radiography