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Detection of Breast Diseases by Sierra Lite Dithering on Mammographic Images
Abstract
This paper proposes an algorithm for the detection of masses and Mcs on digital mammography images by sierra lite dithering which provides a representation of the region of interest and highlights the other bodies present on the image. The results are highly accurate, allowing better detection of breast pathologies (mass and micro-calcification). A filtering step (median filter) is started first to eliminate noise, then a Sierra Lite dithering step is used to detect masses and Mcs. Then a morphological opening step is used here to eliminate the noise generated by the dithering. Finally, the Prewitt contour detection is used to highlight the masses and Mcs. The tests were conducted on the database of a Health and Internet clinic. The results obtained show the robustness of the proposed dithering method. In order to evaluate the efficiency of our pathology detector, we performed a evaluation with a rate of 98.04% for the detection of masses and 70% for the detection of Mcs. A radiologist from the health clinic evaluated the results and considered them acceptable for CAD. Moreover, we point out that it also works on all devices and is capable of real-time image processing.
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