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  • Semiautomatic detection of tumoral zone
    Zagrouba, Ezzeddine ; Barhoumi, Walid
    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 adaptivethresholding. 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 cooperationof two complementary approaches. This allows us to carry out a pertinent approach for the detection of sick zone in MRI images.
    Vir: Image analysis & stereology : official journal of the International Society for Stereology. - ISSN 1580-3139 (Vol. 21, no. 1, mar. 2002, str. 13-18)
    Vrsta gradiva - članek, sestavni del
    Leto - 2002
    Jezik - angleški
    COBISS.SI-ID - 14502617

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    Digitalna knjižnica Slovenije - dLib.si

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