Abstract
Computer-Aided Diagnosis (CAD) systems are widely used to classify skin lesions in dermoscopic images. The segmentation of the lesion area is the initial and key step to automate this process using a CAD system. In this paper, an improved segmentation algorithm is developed based on the following steps: (1) color space transform to the perception-oriented CIECAM02 color model, (2) preprocessing step to correct specular reflection, (3) contrast enhancement using an homomorphic transform filter (HTF) and nonlinear sigmoidal function (NSF) and (4) segmentation with relative entropy (RE) and active contours model (ACM). To validate the proposed technique, comparisons with other three state-of-the-art segmentation algorithms were performed for 210 non-melanoma lesions. From these experiments, an average true detection rate of 91.01, false positive rate of 6.35 and an error probability of 7.8 were obtained. These experimental results indicate that the proposed technique is useful for CAD systems to detect non-melanoma skin lesions in dermoscopy images.
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Abbas, Q., Fondón, I., Sarmiento, A., Emre Celebi, M. (2014). An Improved Segmentation Method for Non-melanoma Skin Lesions Using Active Contour Model. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2014. Lecture Notes in Computer Science(), vol 8815. Springer, Cham. https://doi.org/10.1007/978-3-319-11755-3_22
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DOI: https://doi.org/10.1007/978-3-319-11755-3_22
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