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27 September 2016 Comparison of algorithms for automatic border detection of melanoma in dermoscopy images
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Melanoma is one of the most rapidly accelerating cancers in the world [1]. 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 [3]. 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 [4] 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.
Conference Presentation
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Sowmya Srinivasa Raghavan, Ravneet Kaur, and Robert LeAnder "Comparison of algorithms for automatic border detection of melanoma in dermoscopy images", Proc. SPIE 9971, Applications of Digital Image Processing XXXIX, 997105 (27 September 2016);

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