A Computer Assisted Diagnostic System for Mass Detection

Abstract

Computer assisted diagnostic (CAD) methods have been proposed as a “second opinion” strategy for breast cancer screening using digital mammography. The reported methods have included the detection of either microcalcification clusters or masses [1]. Mass detection poses a more difficult problem compared to microcalcification cluster detection because masses are often: (a) of varying size, shape, and density, (b) exhibit poor image contrast, (c) are highly connected to the surrounding parenchymal tissue density, particularly for spiculated lesions, and (d) are surrounded by non-uniform tissue background with similar characteristics [2]–[4]. The segmentation of masses and the computation of related pixel intensity, morphological, and directional texture features poses difficult problems in terms of improved feature extraction methods, as required for classification methods that distinguish masses from normal tissues. Improved and robust feature extraction has not been emphasizes in the literature. Examples include features such as mass shape or mass margin analysis, or spiculations for spiculated lesions [5]. Mass detection is proposed here as a good clinical model for the motivation for proposing a new class of adaptive CAD methods for image preprocessing, to improve feature extraction. The methods proposed are useful for other CAD applications such as the detection of microcalcifications and lung nodules.KeywordsTienilic AcidMicrocalcification ClusterDirect Vasodilating ActivityRobust Feature ExtractionSpiculated LesionThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.