Analysis of the effects of discrete wavelet compression on automated mammographic mass shape classification
L.M. Bruce, Ravi Kalluri · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1999
This pilot study investigates the effect of discrete wavelet compression on automated mammographic mass shape classification. Commonly used shape features are extracted from masses for uncompressed and compressed images. These features include radial distance mean, standard deviation, entropy, zero-crossing count, roughness index, area-ratio, and compactness. The effects of the compression on these features are analyzed. Next, linear discriminant analysis is used to appropriately weight the features, and a minimum Euclidean distance classifier is used to separate the mass shapes into three classes: round, nodular, and stellate. The classification results are compared between the uncompressed and compressed images.