Friday 29th of March 2024
 

Textural Approach for Mass Abnormality Segmentation in Mammographic Images


Khamsa Djaroudib, Abdelmalik Taleb Ahmed and Abdelmadjid Zidani

Mass abnormality segmentation is a vital step for the medical diagnostic process and is attracting more and more the interest of many research groups. Currently, most of the works achieved in this area have used the Gray Level Co-occurrence Matrix (GLCM) as texture features with a region-based approach. These features come in previous phase for segmentation stage or are using as inputs to classification stage. The work discussed in this paper attempts to experiment the GLCM method under a contour-based approach. Besides, we experiment the proposed approach on various tissues densities to bring more significant results. At this end, we explored some challenging breast images from BIRADS medical Data Base. Our first experimentations showed promising results with regard to the edges mass segmentation methods. This paper discusses first the main works achieved in this area. Sections 2 and 3 present materials and our methodology. The main results are showed and evaluated before concluding our paper.

Keywords: Textural approach, mammography, Mass segmentation, contour, tissues, Gray Level co-occurrence Matrix (GLCM).

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ABOUT THE AUTHORS

Khamsa Djaroudib
Currently a professor Assistant at the University of Batna (Algeria) and member of LaStic laboratory. Her thesis research work is mainly concerned with computer vision and medical image processing.

Abdelmalik Taleb Ahmed
Currently a Professor at the University of Valenciennes, France.

Abdelmadjid Zidani
Currently a Professor at the University of Batna, Algeria.


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