Detection of masses in mammographic images using geometry, Simpson's Diversity Index and SVM
André Nunes, Aristófanes Corrêa Silva, Anselmo Cardoso de Paiva · International Journal of Signal and Imaging Systems Engineering · 2010
This paper presents a computational methodology to detect masses in mammographic images. In the first step, the K-means clustering algorithm and the template-matching technique are used to detect suspicious regions. Next, geometry and texture features of each region are extracted. Texture is described using Simpson's Diversity Index, which is used in Ecology to measure the biodiversity of an ecosystem. Finally, the information of texture is used by Support Vector Machine (SVM) to classify the suspicious regions into two classes: masses and non-masses. The tests demonstrate that the methodology has 83.94% of accuracy, 83.24% of sensitivity and 84.14% of specificity.