Automatic Imaging System With Decision Support for Inspection of Pigmented Skin Lesions and Melanoma Diagnosis

Abstract

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Automated Imaging System for Pigmented Skin Lesion Diagnosis

“…Results shows that geometric features are usually efficient in analayzing region of interest shape in a binary image. This study computed 21 geometric features [9,32] to describe a set of properties for the segmented binary image of PSL. Geometric features were promising in describing the lesion physical structure, but it missed the signifigance of tumor color, brightness or luminance that could be aguide to distinguish PSL true specification.…”

“…Higher or second order statistical features are based on statistical parameters such as the Spatial Gray Level Dependence Method (co-occurrence matrices), the Gray Level Difference Method, and the Gray Level Run Length Matrices [39,40]. One of those examples proposed for that study is the gray level co-occurrence matrix, as it is most popular texture analysis used previously for discrimination of melanoma [9,41]. The choice of Haralick features [42,43] based on GCMs was made considering their proven applicability to analyze objects with irregular outlines [44].…”

“…Based on the co-occurrence matrix, many different texture descriptors may be computed. To reduce the computational complexity, only four most relevant features were selected out of 21 GLCM features presented above, namely Energy, Homogeneity, Correlation and Contrast based on their classification accuracy, computational cost and previously usage in literature [9]. About 88 features were extracted through the present section, where each of the four features was extracted out of each color component of the 7 color spaces (7×3×4), In addition to the four features extracted from the gray scale image of the true space on RGB color.…”

“…Thirteen significant features were selected, that discriminate the 2 groups of By the beginning of 2009, José Fernández Alcón, et al proposed efficient automated system [9] for diagnosis of PSL. The system supports conventional clinical images for skin lesions acquired using digital cameras (Consumer level), in addition to a decision support component.…”

MED-NODE: A computer-assisted melanoma diagnosis system using non-dermoscopic images

“…All those features are subsequently used to train a mono-layer perceptron classifier and yield a system with sensitivity of 75.1% and specificity of 83.1% on a dataset consisting of 125 benign and 75 malignant lesions. The system proposed by Alcon et al (2009) also uses simple digital images as well as context knowledge, such as skin type, age, gender, and the affected body part in order to classify lesions as benign or malignant. This system also attempts to extract features conforming with the ABCD rule and includes steps for preprocessing and segmenting the lesions.…”