ABSTRACT
Black skin medical images generally show very low contrast. Being in a global initiative of characterisation of black skin horny layer (stratum corneum) by digital images analysis, the authors in this study proposed a four-step approach. The first step consists of differentiation between probable healthy skin regions and those affected. For that, they used an automatic classification system based on multilayer perceptron artificial neural networks. The network has been trained with texture and colour features. Best features selection and network architecture definition were done using sequential network construction algorithm-based method. After classification, selected regions undergo a colour transformation, in order to increase the contrast with the lesion region. Thirdly, created colour information serves as the basis for a modified fuzzy c-mean clustering algorithm to perform segmentation. The proposed method, named neural network-based fuzzy clustering, was applied to many black skin lesion images and they obtained segmentation rates up to 94.67%. The last stage consists in calculating characteristics. Eight parameters are concerned: uniformity, standard deviation, skewness, kurtosis, smoothness, entropy, and average pixel values calculated for red and blue colour channels. All developed methods were tested with a database of 600 images and obtained results were discussed and compared with similar works.
ABSTRACT
We propose and fabricate a novel ring-core photonic crystal fiber made of a circular ring core surrounded by a cladding constituted of air holes organized in a first circular ring surrounded by hexagonal ones. The fiber efficiently supports four different groups of orbital angular momentum (OAM) modes. The effective indices of spin-orbit aligned and spin-orbit anti-aligned modes in the same OAM modes group are separated by at least 2.13×10-3 at 1550 nm. The realized fiber is expected to be a good platform for applications involving OAM modes.
