Knowledge-discovery incorporated evolutionary search for microcalcification detection in breast cancer diagnosis

The presence of microcalcifications (MCs), clusters of tiny calcium deposits that appear as small bright spots in a mammogram, has been considered as a very important indicator for breast cancer diagnosis. Much research has been performed for developing computer-aided systems for the accurate identification of MCs, however, the computer-based automatic detection of MCs has been shown difficult because of the complicated nature of surrounding of breast tissue, the variation of MCs in shape, orientation, brightness and size.

This paper presents a new approach for the effective detection of MCs by incorporating a knowledge-discovery mechanism in the genetic algorithm (GA). In the proposed approach, called knowledge-discovery incorporated genetic algorithm (KD-GA), the genetic algorithm is used to search for the bright spots in mammogram and a knowledge-discovery mechanism is integrated to improve the performance of the GA. The function of the knowledge-discovery mechanism includes evaluating the possibility of a bright spot being a true MC, and adaptively adjusting the associated fitness values. The adjustment of fitness is to indirectly guide the GA to extract the true MCs and eliminate the false MCs (FMCs) accordingly.

The experimental results demonstrate that the incorporation of knowledge-discovery mechanism into the genetic algorithm is able to eliminate the FMCs and produce improved performance comparing with the conventional GA methods. Furthermore, the experimental results show that the proposed KD-GA method provides a promising and generic approach for the development of computer-aided diagnosis for breast cancer.