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Proceedings Paper

Hybrid framework based on evidence theory for blood cell image segmentation
Author(s): Ismahan Baghli; Amir Nakib; Elie Sellam; Mourtada Benazzouz; Amine Chikh; Eric Petit
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Paper Abstract

The segmentation of microscopic images is an important issue in biomedical image processing. Many works can be found in the literature; however, there is not a gold standard method that is able to provide good results for all kinds of microscopic images. Then, authors propose methods for a given kind of microscopic images. This paper deals with new segmentation framework based on evidence theory, called ESA (Evidential Segmentation Algorithm) to segment blood cell images. The proposed algorithm allows solving the segmentation problem of blood cell images. Herein, our goal is to extract the components of a given cell image by using evidence theory, that allows more flexibility to classify the pixels. The obtained results showed the efficiency of the proposed algorithm compared to other competing methods.

Paper Details

Date Published: 13 March 2014
PDF: 8 pages
Proc. SPIE 9038, Medical Imaging 2014: Biomedical Applications in Molecular, Structural, and Functional Imaging, 903815 (13 March 2014); doi: 10.1117/12.2042142
Show Author Affiliations
Ismahan Baghli, Lab. GBM, Univ. Abou Bekr Belkaid Tlemcen (Algeria)
Amir Nakib, Lab. Lissi, Univ. Paris-Est Crétail Val de Marne (France)
Elie Sellam, Lab. LISSI, Univ. Paris-Est Crétail Val de Marne (France)
Mourtada Benazzouz, Lab. GBM, Univ. Abou Bekr Belkaid Tlemcen (Algeria)
Amine Chikh, Lab. GBM, Univ. Abou Bekr Belkaid Tlemcen (Algeria)
Eric Petit, Lab. LISSI, Univ. Paris-Est Crétail Val de Marne (France)


Published in SPIE Proceedings Vol. 9038:
Medical Imaging 2014: Biomedical Applications in Molecular, Structural, and Functional Imaging
Robert C. Molthen; John B. Weaver, Editor(s)

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