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Detection of Microcalcifications on Mammograms
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Author(s): Rachida Touami (Laboratoire SIMPA, Département d'Informatique, Faculté des Mathématiques et d'Informatique, USTO-MB, Bir El Djir, Algeria), Karima KIES (Laboratoire SIMPA, Département d'Informatique, Faculté des Mathématiques et d'Informatique, USTO-MB, Bir El Djir, Algeria)and Nacéra Benamrane (Laboratoire SIMPA, Département d'Informatique, Faculté des Mathématiques et d'Informatique, USTO-MB, Bir El Djir, Algeria)
Copyright: 2020
Volume: 12
Issue: 1
Pages: 12
Source title:
International Journal of Software Science and Computational Intelligence (IJSSCI)
Editor(s)-in-Chief: Brij Gupta (Asia University, Taichung City, Taiwan)and Andrew W.H. Ip (University of Saskatchewan, Canada)
DOI: 10.4018/IJSSCI.2020010105
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Abstract
Breast cancer is a common disease of women. The number of new cases diagnosed in Algeria is increasingly high and it is the first cause of cancer related deaths for women. The microcalcifications are considered the primary sign of breast cancer. The early detection of these allows doctors to take the necessary measures for the treatment of this pathology. Medical imaging has made possible enormous progress in the field of diagnosis and provides an important contribution to the care of patients. This article proposes an approach for the segmentation and detection of microcalcifications in mammographic images based on wavelets, K- means, and the windows of Parzen, in order to detect the presence of breast cancer as early as possible and to avoid radical treatment such as the removal of the breast.
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