Centroid sensitivity of wavelet-based shape features

Many shape features are based on a one-dimensional 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 used 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.