Approaches for automated detection and classification of masses in mammograms
Pattern Recognition
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Mammograms can depict most of the significant changes of breast disease. The primary radiographic signs of cancer are masses (its density, size, shape, borders), spicular lesions and calcification content. These features may be extracted according to their coherence and orientation and can provide important visual cues for radiologists to locate suspicious areas without generating false positives. An artifact free enhancement algorithm based on overcomplete multiscale wavelet analysis is presented. The novelty of this algorithm lies in its detection of directional features and removal of unwanted perturbations. Compared to existing multiscale enhancement approaches, images processed with this method appear more familiar to radiologists and naturally close to the original mammogram.