Automatic recognition of microcalcifications in mammography images through fractal texture analysis

Abstract

Mammography images are widely used for detection of microcalcifications (MCs), which constitute the early stage of breast cancer. Moreover, these images allow the medical specialist to perform a timely diagnosis and to prevent complications in patients. Automatic identification of MCs in mammography images may be useful as a decision support given by a specialist. In this paper, we construct a mammography image database with medical validation and expert labeling. The test subjects are from a local population located in the Eje cafetero, Colombia. Also, we present a methodology for automatic recognition of microcalcifications based on segmentation with fractal texture analysis (SFTA) and a support vector machine (SVM). For a comparison framework with the state of the art, we compare our methodology with the local binary patterns (LBP) method, that is widely applied in digital images processing. Results show that SFTA methodology for recognition of MCs achieves an accuracy over 92.5% improving significatively when compared to LBP. Also, our database satisfies the epidemiological parameters to represent a local population.