Possibilistic clustering criterion for the segmentation of magnetic resonance images

Authors:
Hector Vargas;Vittorio Zanella
Affiliations:
Faculty of Computer Science, Universidad Popular Autónoma del Estado de Puebla, Puebla, Mexico;Faculty of Computer Science, Universidad Popular Autónoma del Estado de Puebla, Puebla, Mexico
Venue:
ISCGAV'04 Proceedings of the 4th WSEAS International Conference on Signal Processing, Computational Geometry & Artificial Vision
Year:
2004

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Abstract

Combining the possibilistic methodology with topological concepts, we present a new clustering criterion for segmenting magnetic resonance images. The clustering criterion generates a covering of the image, thereby permitting the perception of intersections of different brain tissues, which gives the specialist another way of representing data in order to refine his/her perception in said structures. Furthermore, an analysis is carried out regarding the validation of the clusters generated by the algorithm, such as the grade of compactification of the objects in each class, as well as the separability of classes.