Proceedings Article | 15 March 2006
Proc. SPIE. 6144, Medical Imaging 2006: Image Processing
KEYWORDS: Image segmentation, Skin, Medical imaging, Fuzzy logic, Detection and tracking algorithms, Image processing algorithms and systems, Melanoma, Databases, Shape analysis, Visual analytics
Automatic segmentation of skin lesions in clinical images is a very
challenging task; it is necessary for visual analysis of the edges,
shape and colors of the lesions to support the melanoma diagnosis,
but, at the same time, it is cumbersome since lesions (both naevi
and melanomas) do not have regular shape, uniform color, or univocal
structure. Most of the approaches adopt unsupervised color
clustering. This works compares the most spread color clustering
algorithms, namely median cut, k-means, fuzzy-c means and mean shift
applied to a method for automatic border extraction, providing an
evaluation of the upper bound in accuracy that can be reached with
these approaches. Different tests have been performed to examine the
influence of the choice of the parameter settings with respect to
the performances of the algorithms. Then a new supervised learning
phase is proposed to select the best number of clusters and to
segment the lesion automatically. Examples have been carried out in
a large database of medical images, manually segmented by
dermatologists. From these experiments mean shift was resulted the
best technique, in term of sensitivity and specificity. Finally, a
qualitative evaluation of the goodness of segmentation has been
validated by the human experts too, confirming the results of the
quantitative comparison.
