A New Approach to Image Segmentation Mammogram
American Journal of Nano Research and Applications
Volume 3, Issue 4, July 2015, Pages: 78-81
Received: Jun. 19, 2015; Accepted: Jul. 7, 2015; Published: Jul. 17, 2015
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Authors
Mohammed Rmili, LAboratory of Research on Optimization of Emergent Systems, Network and Imaging of computer science Department of ChouaïbDoukkali University, EL Jadida, Morocco; LAboratory of Mathematics Applied on Physics and Industry, Mathematics Department of ChouaïbDoukkali University, EL Jadida, Morocco
Abdellatif Siwane, Radiology Departement Faculty of Medicine and Pharmacy of Hassan II University and Central Service of Radiology Ibn Rochd University Hospital Casablanca, Morocco
Fatiha Adnani, LAboratory of Mathematics Applied on Physics and Industry, Mathematics Department of ChouaïbDoukkali University, EL Jadida, Morocco
Fatiha Essodegui, Radiology Departement Faculty of Medicine and Pharmacy of Hassan II University and Central Service of Radiology Ibn Rochd University Hospital Casablanca, Morocco
Abdelmajid El Moutaouakkil, LAboratory of Research on Optimization of Emergent Systems, Network and Imaging of computer science Department of ChouaïbDoukkali University, EL Jadida, Morocco
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Abstract
Breast cancer continues to be one of the main causes of death among women. Various studies have confirmed that the early detection of sub-clinical cancers may improve the prognosis. X-ray mammography in this case is the best diagnostic technique. It’s based on the interaction of a cone beam X-ray with the mole tissue. The projection image obtained can be analyzed qualitatively by the radiologists. But, an automatic treatment and quantitative analysis of this kind of images is suitable. For this reason several studies are conducted to develop tools to help with diagnosis of this disease (CAD: Computer-Assisted Diagnosis). We propose in this paper a new method to segment mammographic images based partly on a pyramidal architecture. The original image is fragmented (quadtree) initially to homogeneous regions. Each region is then associated with a peak of graph. It gathers data within homogeneous groups named regions classes’ c, then we use HCA (Hierarchical classification ascendant) and k-means to find the optimal partition for the largest possible value of c at the initial stage. This technique gives good results, and allows calculating morphological parameters of the breast cancer.
Keywords
Mamography, Image Segmentation, K-means, Irregular Pyramid, HCA
To cite this article
Mohammed Rmili, Abdellatif Siwane, Fatiha Adnani, Fatiha Essodegui, Abdelmajid El Moutaouakkil, A New Approach to Image Segmentation Mammogram, American Journal of Nano Research and Applications. Vol. 3, No. 4, 2015, pp. 78-81. doi: 10.11648/j.nano.20150304.12
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