4th International Conference on Information Technology in Bio- and Medical Informatics, ITBAM 2013, Prague, Czech Republic, 28 August 2013, vol.8060 LNCS, pp.103-105
Microcalcifications have been mainly targeted as the earliest sign of breast cancer, thus their early detection is very important process. Since their size is very small and sometimes hidden by breast tissue, computer-based detection output can assist the radiologist to increase the diagnostic accuracy. This paper presents a research on mammography images using rough entropy and fuzzy approach. Our proposed method includes two main steps; preprocessing and segmentation. In the first step, we have implemented mammography image enhancement using wavelet transform, CLAHE and anisotropic diffusion filter then rough pectoral muscle extraction for false region reduction and better segmentation. In the second step, we have used Rough entropy to define a threshold and then, fuzzy based microcalcification enhancement, after these microcalcifications have been segmented using an iterative detection algorithm. By the combination of these methods, a novel hybrid algorithm has been developed and successful results have been obtained on MIAS database. © 2013 Springer-Verlag.