Melanoma is one of the most rapidly accelerating cancers in the world . Early diagnosis is critical to an effective cure.
We propose a new algorithm for more accurately detecting melanoma borders in dermoscopy images. Proper border
detection requires eliminating occlusions like hair and bubbles by processing the original image. The preprocessing step
involves transforming the RGB image to the CIE L*u*v* color space, in order to decouple brightness from color
information, then increasing contrast, using contrast-limited adaptive histogram equalization (CLAHE), followed by
artifacts removal using a Gaussian filter. After preprocessing, the Chen-Vese technique segments the preprocessed
images to create a lesion mask which undergoes a morphological closing operation. Next, the largest central blob in the
lesion is detected, after which, the blob is dilated to generate an image output mask. Finally, the automatically-generated
mask is compared to the manual mask by calculating the XOR error . Our border detection algorithm was developed
using training and test sets of 30 and 20 images, respectively. This detection method was compared to the SRM method
 by calculating the average XOR error for each of the two algorithms. Average error for test images was 0.10, using
the new algorithm, and 0.99, using SRM method. In comparing the average error values produced by the two algorithms,
it is evident that the average XOR error for our technique is lower than the SRM method, thereby implying that the new
algorithm detects borders of melanomas more accurately than the SRM algorithm.