SEMIAUTOMATIC DETECTION OF TUMORAL ZONE

Ezzeddine Zagrouba, Walid Barhoumi

Abstract

This paper describes a robust method based on the cooperation of fuzzy classification and regions segmentation algorithms, in order to detect the tumoral zone in the brain Magnetic Resonance Imaging (MRI). On one hand, the classification in fuzzy sets is done by the Fuzzy C-Means algorithm (FCM), where a study of its different parameters and its complexity has been previously realised, which led us to improve it. On the other hand, the segmentation in regions is obtained by an hierarchical method through adaptive thresholding. Then, an operator expert selects a germ in the tumoral zone, and the class containing the sick zone is localised in return for the FCM algorithm. Finally, the superposition of the two partitions of the image will determine the sick zone. The originality of our approach is the parallel exploitation of different types of information in the image by the cooperation of two complementary approaches. This allows us to carry out a pertinent approach for the detection of sick zone in MRI images.

Keywords
FCM; fuzzy classification; MRI; segmentation; tumour brain

Full Text:

PDF


DOI: 10.5566/ias.v21.p13-18

Image Analysis & Stereology
EISSN 1854-5165 (Electronic version)
ISSN 1580-3139 (Printed version)