Automated Skin Lesion Preprocessing Model of Dermoscopic Images Towards Melanoma Detection

Abstract

Melanoma is lethal form of skin cancer. For unbiased diagnosis of melanoma skin cancer an automated and computerized image analysis system is prime requirement. Clinicians examine the digital photographs taken from dermoscopy lens to detect skin cancer. The images captured usually contains various noise artifacts like low contrast hair objects, ink markers, vignette effect, ebony frames. These artifacts often affect results of skin lesion segmentation and lesion classification analysis. In this paper, we propose an automated image pre-processing model to remove noise artifacts like thin & thick hair objects, surgical ink markers, dark vignette effect, ebony frames from the images. To segment required lesion region and detect lesion edges we applied Otsu thresholding and Canny edge technique. We also applied these techniques on preprocessed image and original image with noise artifacts to check the efficiency of our model. We discuss various methodologies and results acquired at each phase. Based on the experimental results, the proposed model can be used successfully in removal of noise artifacts from dermoscopic images. In future, it can be used to build a reliable and accurate computer aided diagnostic systems.