  Digital image processing techniques have wide applications in different
scientific fields including the medicine. By use of image processing
algorithms, physicians have been more successful in diagnosis of different
diseases and have achieved much better treatment results. In this paper, we
propose an automatic method for segmenting the skin lesions and extracting
features that are associated to them. At this aim, a combination of Speeded-Up
Robust Features (SURF) and Active Contour Model (ACM), is used. In the
suggested method, at first region of skin lesion is segmented from the whole
skin image, and then some features like the mean, variance, RGB and HSV
parameters are extracted from the segmented region. Comparing the segmentation
results, by use of Otsu thresholding, our proposed method, shows the
superiority of our procedure over the Otsu theresholding method. Segmentation
of the skin lesion by the proposed method and Otsu thresholding compared the
results with physician's manual method. The proposed method for skin lesion
segmentation, which is a combination of SURF and ACM, gives the best result.
For empirical evaluation of our method, we have applied it on twenty different
skin lesion images. Obtained results confirm the high performance, speed and
accuracy of our method.
