Improving mass detection by adaptive and multiscale processing in digitized mammograms

A new CAD mass detection system was developed using adaptive and multi-scale processing methods for improving detection sensitivity/specificity, and its robustness to the variation in mammograms. The major techniques developed in system design include: (1) image standardization by applying a series of preprocessing to remove extrinsic signal, extract breast area, and normalize the image intensity; (2) multi- mode processing by decomposing image features using directional wavelet transform and non-linear multi-scale representation using anisotropic diffusion; (3) adaptive processing in image segmentation using localized adaptive thresholding and adaptive clustering; and (4) combined `hard'-`soft' classification by using a modified fuzzy decision tree and committee decision-making method. Evaluations and comparisons were taken with a training dataset containing 30 normal and 47 abnormal mammograms with totally 70 masses, and an independent testing dataset consisting of 100 normal images, 39 images with 48 minimal cancers and 25 images with 25 benign masses. A high detection performance of sensitivity TP equals 93% with false positive rate FP equals 3.1 per image and a good generalizability with TP equals 80% and FP equals 2.0 per image are obtained.