Analysis of the effects of discrete wavelet compression on automated mammographic mass shape classification

Document Type

Conference Proceeding


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.


Classification--Computer programs; Diagnostic imaging; Image analysis; Image processing; Wavelets (Mathematics)


Electrical and Computer Engineering | Engineering | Signal Processing | Systems and Communications


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