Browsing by Author "Beiji, Zou"

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    A novel robust and fast Segmentation of the Color Images using Fuzzy Classification C-means
    (IEEE, 2010) Musau, Felix; Toure, Mohamed Lamine; Beiji, Zou
    This paper brings out a method for segmentation of color images based on fuzzy classification. It proceeds in a first step by a fine segmentation using the algorithm of fuzzy cmeans (FCM). The method then applies a test fusion of fuzzy classes. The result is a coarse segmentation, where each region is the union of elementary regions grown from FCM. The fuzzy C-Means (FCM) clustering is an iterative partitioning method that produces optimal c-partitions, the standard FCM algorithm takes a long time to partition a large data set. The proposed FCM program must read the entire data set into a memory for processing. Our results show that the system performance is robust to different types of images.
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    Advanced Algorithm for Brain Segmentation using Fuzzy to Localize Cancer and Epilespy Region
    (IEEE, 2010) Musau, Felix; Beiji, Zou; Toure, Mohamed Lamine; Camara, Aboubacar Damaye
    The research which addresses the diseases of the brain in the field of the vision by computer is one of the challenges in recent times in medicine, the engineers and researchers recently launched challenges to carry out innovations of technology pointed in imagery. This paper focuses on a new algorithm for brain segmentation of color images based on fuzzy classification to diagnose accurately the region of cancer and the area of epilepsy. In a first step it proceeds by a fine segmentation using the algorithm of fuzzy cmeans (FCM). It then applies a test fusion of fuzzy classes. The result is a coarse segmentation, where each region is the union of elementary regions grown from FCM. The fuzzy C-Means (FCM) clustering is an iterative partitioning method that produces optimal c-partitions. The standard FCM algorithm takes a long time to partition a large data set. The proposed FCM program must read the entire data set into a memory for processing. Our results show that the system performance is robust to different types of images.
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    Advanced Algorithm Partitioning of Markov and Color Image Segmentation
    (IEEE, 2010) Musau, Felix; Beiji, Zou; Toure, Mohamed Lamine
    The color vision systems require a first step of classifying pixels in a given image into a discrete set of color classes. In this paper we introduce a new method of algorithm partitioning, and color image segmentation based on similarities or dissimilarities of the pixels. We consider fuzzy segmentation with markov, and normalized cut method. Experiments show these different processes used an effective solution on natural images, and computational efficiency. Finally, the algorithm has proven our process of experiments on gray scale, color, and texture images show promising segmentation results successful