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.

Date Published: 21 May 1999
PDF: 6 pages
Proc. SPIE 3661, Medical Imaging 1999: Image Processing, (21 May 1999); doi: 10.1117/12.348513

Published in SPIE Proceedings Vol. 3661:
Medical Imaging 1999: Image Processing
Kenneth M. Hanson, Editor(s)