Many shape features are based on a 1D function known as the radial distance measure (RDM). These include its mean, standard deviation, zero crossings, entropy, and roughness index. Recently, wavelet-based features, computed via the RDM, have been sued for object shape recognition. In particular the RDM scalar-energy feature is used in this study. We analyze the effects of centroid errors on the RDM- based feature measures listed above by measuring their mean- square-errors. The error analysis is conducted on a set of 60 images consisting of simplistic shapes: ellipses, triangles, rectangles, and pentagons. The error analysis is also conducted on a set of mammograms where mammographic lesions are to be discriminated into the shape classes: circumscribed, irregular, and stellate. These shape classes are typically used to aid in the classification of lesions as either benign or malignant. Sixty pre-segmented mammographic lesions are used in this analysis. A minimum distance classifier is used to classify the lesion shapes. The effects on the traditional feature vectors are compared with the wavelet-based feature vectors. Lastly, the effects of centroid errors are analyzed with respect to classification rates.

Date Published: 26 March 1998
PDF: 9 pages
Proc. SPIE 3391, Wavelet Applications V, (26 March 1998); doi: 10.1117/12.304886

Published in SPIE Proceedings Vol. 3391:
Wavelet Applications V
Harold H. Szu, Editor(s)