26 January 2017 Pigmented skin lesion segmentation based on sparse texture representations
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Proceedings Volume 10160, 12th International Symposium on Medical Information Processing and Analysis; 101600N (2017) https://doi.org/10.1117/12.2256891
Event: 12th International Symposium on Medical Information Processing and Analysis, 2016, Tandil, Argentina
Abstract
Among the most dangerous cancers, there is the Melanoma that affects millions of people. As this is a type of malignant pigmented skin lesion and it can be recognized by medical experts, computer-aided diagnostic systems are developed in order to assist dermatologists in clinical routine. One of the more difficult tasks is to find the right segmentation of lesions whose precision is very important to distinguish benign from malignant cases. In this work, we propose a new method based on sparse representation. First, an alternative representation of the image is obtained from the texture information. A sparse non-negative dictionary is computed and every image is projected onto this space. The reconstruction is calculated using only the most active atoms, which allows to obtaining an enhanced version of the texture where the morphological post-processing can effectively extract the lesion area. The experiments were carried out on a publicly available database and performance was evaluated in terms of segmentation error, accuracy, and specificity. Results showed that this first approach performs better than methods reported in the literature on this same data.
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César E. Martínez, Enrique M. Albornoz, "Pigmented skin lesion segmentation based on sparse texture representations", Proc. SPIE 10160, 12th International Symposium on Medical Information Processing and Analysis, 101600N (26 January 2017); doi: 10.1117/12.2256891; https://doi.org/10.1117/12.2256891
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