Multichannel filtering for texture feature extraction in digital mammograms

Breast cancer is a major cause of cancer deaths among women. Early detection of the primary tumor is an essential and effective method to reduce mortality. In this paper we present a new automated method for detection of rumors in digital mammograms based on the application of multichannel filtering for feature feature extraction. The channel filters are represented by a computationally efficient infinite impulse response (IIR) QMF bank. The texture feature extraction method is applied to detect stellate lesions in mammograms from the MIAS(1) database. The experiments demonstrate that our approach can provide a true detection rate of approximately 86% and 0 false detections per image for fatty-glandular mammograms.