Combined Optimal Wavelet Filters with Morphological Watershed Transform for the Segmentation of Dermoscopic Skin Lesions

In this paper, a technique is proposed to segment skin lesions from dermoscopic images through a combination of watershed transform and wavelet filters. In our technique, eight types of wavelet filters such as Daubechies and bi-orthogonal filters were applied before watershed transform. The resulting image was then classified into two classes: background and foreground. As watershed transform generated many spurious regions on the background, morphological post-processing was conducted. The post-processing split and merged spurious regions depending on a set of predefined criteria. As a result, a binary image was obtained and a boundary around the lesion was drawn. Next, the automatic boundary was compared with the manually delineated boundary by medical experts on 70 images with different types of skin lesions. We have obtained the highest accuracy of 94.61% using watershed transform with level 2 bi-orthogonal 3.3 wavelet filter. Thus, the proposed method has effectively achieved segmentation of the skin lesions, as shown in this paper.