A fuzzy clustering based segmentation system as support to diagnosis in medical imaging.
In medical imaging uncertainty is widely present in data, because of the noise in acquisition and of the partial volume effects originating from the low resolution of sensors. In particular, borders between tissues are not exactly defined and memberships in the boundary regions are intrinsically fuzzy. Therefore, computer assisted unsupervised fuzzy clustering methods turn out to be particularly suitable for handling a decision making process concerning segmentation of multimodal medical images. By using the possibilistic c-means algorithm as a refinement of a neural network based clustering algorithm named capture effect neural network, we developed the possibilistic neuro fuzzy c-means algorithm (PNFCM). In this paper the PNFCM has been applied to two different multimodal data sets and the results have been compared to those obtained by using the classical fuzzy c-means algorithm. Furthermore, a discussion is presented about the role of fuzzy clustering as a support to diagnosis in medical imaging.
Masulli
F
F
Istituto Nazionale per la Fisica della Materia and Dipartimento di Informatica e Scienze dell'Informazio ne, Università di Genova, Italy. masulli@disi.unige.it
Schenone
A
A
D000465:Algorithms; D001921:Brain / Q000473:pathology*; D017143:Fuzzy Logic; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008579:Meningioma / Q000175:diagnosis*; D016571:Neural Networks, Computer