Analysis of the contour structural irregularity of skin lesions using wavelet decomposition

The boundary irregularity of skin lesions is of clinical significance for the early detection of malignant melanomas and to distinguish them from other lesions such as benign moles. The structural components of the contour are of particular importance. To extract the structure from the contour, wavelet decomposition was used as these components tend to locate in the lower frequency sub-bands. Lesion contours were modeled as signatures with scale normalization to give position and frequency resolution invariance. Energy distributions among different wavelet sub-bands were then analyzed to extract those with significant levels and differences to enable maximum discrimination. Based on the coefficients in the significant sub-bands, structural components from the original contours were modeled, and a set of statistical and geometric irregularity descriptors researched that were applied at each of the significant sub-bands. The effectiveness of the descriptors was measured using the Hausdorff distance between sets of data from melanoma and mole contours. The best descriptor outputs were input to a back projection neural network to construct a combined classifier system. Experimental results showed that thirteen features from four sub-bands produced the best discrimination between sets of melanomas and moles, and that a small training set of nine melanomas and nine moles was optimum

Morphological aspects in the diagnosis of skin lesions

En col·laboració amb la Universitat de Barcelona (UB), la Universitat Autònoma de Barcelona (UAB) i l’Institut de Ciències Fotòniques (ICFO)The ABCDE (Asymmetry, Border, Color, Rambla de Sant Nebridi, 10, Diameter and Elevation) rule represents a commonly used clinical guide for the early identification of melanoma. Here we develop a methodology based on an Artificial Neural Network which is trained to stablish a clear differentiation between benign and m lesions. This machine learning approach improves prognosis and diagnosis accuracy rates. align In order to obtain the 6 morphological feature data set for each of the 69 lesions considered, a 3D handheld system is used for acquiring the skin images and an image processing algorithm is applied