Texture analysis is one of the fundamental aspects of human vision by which we differentiate between surfaces and objects. Texture refers to visual patterns or spatial organization of pixels. Texture classification is deals with identifying a given textured region from a given set of texture classes. Each of these regions has unique texture characteristics. Statistical methods are extensively used to determine the texture as a quantitative measure of the arrangement of intensities in a region [1]. Texture and colour of human skin has proven to be the most important aspect of several imaging systems. Human texture reproduction has been very beneficial in medical diagnosis, modeling and cosmetic analysis [1]. The basis on which the human is
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Previously the image is analyzed for extracting hemoglobin and melanin components by independent component analysis. The image has been synthesized for the comparison and the texture contents are also rotten for separating the image of skin into the basic shape and the feature vector based on which the texton (pixel) change is observed to determine the pattern and structure [3]. The filtering method is also introduced which is applied to dermoscopic skin image in a non-linear manner and allows selective image filtering. This feature is highly desirable due to the fact that in most cases of computer aided diagnostic, input images need to be pre-processed (e.g.for brightness normalization, histogram equalization, contrast enhancement, color normalization) and this can results in unwanted artifacts or simply may require human verification. [4]
The rest of paper is organized as follows; in section II the related works based on skin textures analysis is discussed in detail. In section III methodologies used for categorizing skin textures are described. Section IV gives the detail about experimental results of proposed work. In section V describes conclusion and future enhancement of the proposed …show more content…
The disease conditions are recognized by analyzing skin texture images using a set of normalized symmetrical Grey Level Co-occurrence Matrices (GLCM) . GLCM defines the probability of grey level I occurring in the neighborhood of another grey level j at a distance d in direction θ. The system is tested using 180 images pertaining to three dermatological skin conditions viz. Dermatitis, Eczema, Urticaria. An accuracy of 96.6% is obtained using a multilayer perceptron (MLP) as a
Previously the image is analyzed for extracting hemoglobin and melanin components by independent component analysis. The image has been synthesized for the comparison and the texture contents are also rotten for separating the image of skin into the basic shape and the feature vector based on which the texton (pixel) change is observed to determine the pattern and structure [3]. The filtering method is also introduced which is applied to dermoscopic skin image in a non-linear manner and allows selective image filtering. This feature is highly desirable due to the fact that in most cases of computer aided diagnostic, input images need to be pre-processed (e.g.for brightness normalization, histogram equalization, contrast enhancement, color normalization) and this can results in unwanted artifacts or simply may require human verification. [4]
The rest of paper is organized as follows; in section II the related works based on skin textures analysis is discussed in detail. In section III methodologies used for categorizing skin textures are described. Section IV gives the detail about experimental results of proposed work. In section V describes conclusion and future enhancement of the proposed …show more content…
The disease conditions are recognized by analyzing skin texture images using a set of normalized symmetrical Grey Level Co-occurrence Matrices (GLCM) . GLCM defines the probability of grey level I occurring in the neighborhood of another grey level j at a distance d in direction θ. The system is tested using 180 images pertaining to three dermatological skin conditions viz. Dermatitis, Eczema, Urticaria. An accuracy of 96.6% is obtained using a multilayer perceptron (MLP) as a