Multiresolution-based segmentation of calcifications for the early detection of breast cancer

Clusters of microcalcifications in a mammogram may be an early indication of breast cancer. Unfortunately, due to size, shape and limited contrast from surrounding normal tissue, microcalcifications can occasionally be hard to detect in computer-aided detection (CAD) systems. These CAD systems can also be slow compared to a radiologist's performance when reviewing film-screen mammography. The research described here investigates a rapid, multiresolution-based approach combined with wavelet analysis to provide an accurate segmentation of potential calcifications. An initial multiresolution approach to fuzzy c-means (FCM) segmentation is employed to rapidly distinguish medically significant tissues. Tissue areas chosen for high-resolution analysis are broken into multiple windows. Within each window, wavelet analysis is used to generate a contrast image, and a local FCM segmentation generates an estimate of local intensity. A simple two-rule fuzzy system then combines intensity and contrast information to derive fuzzy memberships of pixels in the high-contrast, bright pixel class. A double threshold is finally applied to this fuzzy membership to detect and segment calcifications. This sequence of steps is shown to approach detection rates of conventional classifier designs and may therefore be useful as a pre-processing module for these systems to improve speed. Results are reported for 25 images obtained from the Digital Database for Screening Mammography (DDSM).