Validating method classifying digitally modulated signals
We introduce a computer-assisted detection (CAD) system for the automated detection of breast masses in screening mammograms.The system targets the directional behavior of the neighborhood pixels surrounding a reference image pixel.Our results indicate that the dual CAD system can improve the performance of mass detection on mammograms.We present a new algorithm and preliminary results for classifying lesions into BI-RADS shape categories: round, oval, lobulated, or irregular.
We have invented these filters specifically for detecting spiculated masses and architectural distortions that are marked by converging lines or spiculations.
These filters are highly specific narrowband filters, which are designed to match the expected structures of these abnormalities and form a new class of wavelet-type filterbanks derived from optimal theories of filtering.
A key aspect of this work is that each parameter of the filter has been incorporated to capture the variation in physical characteristics of spiculated masses and architectural distortions and that the parameters of the stage-one detection algorithm are determined by the physical measurements.
The method was tested on a set of 25 images of each type and we obtained a classification accuracy of 78% for classifying masses as oval or round and an accuracy of 72% for classifying masses as lobulated or round.
Mass detection algorithms generally consist of two stages.