Method for robust demarcation of the basal cell carcinoma (BCC) is presented employing novel dependent component analysis (DCA)-based approach to unsupervised segmentation of the red-green-blue (RGB) fluorescent image of the BCC. It exploits spectral diversity between the BCC and the surrounding tissue. DCA represents an extension of the independent component analysis (ICA) and is necessary to account for statistical dependence induced by spectral similarity between the BCC and surrounding tissue. Robustness to intensity fluctuation is due to the scale invariance property of DCA algorithms. By comparative performance analysis with state-of-the-art image segmentation methods such as active contours (level set), K-means clustering, non-negative matrix factorization and ICA we experimentally demonstrate good performance of DCA-based BCC demarcation in demanding scenario where intensity of the fluorescent image has been varied almost two-orders of magnitude.

Date Published: 27 March 2009
PDF: 8 pages
Proc. SPIE 7259, Medical Imaging 2009: Image Processing, 72594Q (27 March 2009); doi: 10.1117/12.806404

Published in SPIE Proceedings Vol. 7259:
Medical Imaging 2009: Image Processing
Josien P. W. Pluim; Benoit M. Dawant, Editor(s)