  In this paper, a novel approach for automatic segmentation and classification
of skin lesions is proposed. Initially, skin images are filtered to remove
unwanted hairs and noise and then the segmentation process is carried out to
extract lesion areas. For segmentation, a region growing method is applied by
automatic initialization of seed points. The segmentation performance is
measured with different well known measures and the results are appreciable.
Subsequently, the extracted lesion areas are represented by color and texture
features. SVM and k-NN classifiers are used along with their fusion for the
classification using the extracted features. The performance of the system is
tested on our own dataset of 726 samples from 141 images consisting of 5
different classes of diseases. The results are very promising with 46.71% and
34% of F-measure using SVM and k-NN classifier respectively and with 61% of
F-measure for fusion of SVM and k-NN.
