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Proceedings Paper

Classification of melanoma using wavelet transform-based optimal feature set
Author(s): Ronn P. Walvick; Ketan Patel; Sachin V. Patwardhan; Atam P. Dhawan
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Paper Abstract

The features used in the ABCD rule for characterization of skin lesions suggest that the spatial and frequency information in the nevi changes at various stages of melanoma development. To analyze these changes wavelet transform based features have been reported. The classification of melanoma using these features has produced varying results. In this work, all the reported wavelet transform based features are combined to form a single feature set. This feature set is then optimized by removing redundancies using principal component analysis. A feed forward neural network trained with the back propagation algorithm is then used in the classification process to obtain better classification results.

Paper Details

Date Published: 12 May 2004
PDF: 8 pages
Proc. SPIE 5370, Medical Imaging 2004: Image Processing, (12 May 2004); doi: 10.1117/12.536013
Show Author Affiliations
Ronn P. Walvick, New Jersey Institute of Technology (United States)
Ketan Patel, New Jersey Institute of Technology (United States)
Sachin V. Patwardhan, New Jersey Institute of Technology (United States)
Atam P. Dhawan, New Jersey Institute of Technology (United States)


Published in SPIE Proceedings Vol. 5370:
Medical Imaging 2004: Image Processing
J. Michael Fitzpatrick; Milan Sonka, Editor(s)

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