A computational approach for detecting pigmented skin lesions in macroscopic images

Abstract

a b s t r a c tSkin cancer is considered one of the most common types of cancer in several countries and its incidence rate has increased in recent years. Computational methods have been developed to assist dermatologists in early diagnosis of skin cancer. Computational analysis of skin lesion images has become a challenging research area due to the difficulty in discerning some types of skin lesions. A novel computational approach is presented for extracting skin lesion features from images based on asymmetry, border, colour and texture analysis, in order to diagnose skin lesion types. The approach is based on an anisotropic diffusion filter, an active contour model without edges and a support vector machine. Experiments were performed regarding the segmentation and classification of pigmented skin lesions in macroscopic images, with the results obtained being very promising.

The role of AI classifiers in skin cancer images

“…The top result of 88.9% ACC was achieved when Probability Averaging Fusion was used to combine the classifiers results, instead of Majority Voting. The RBF kernel was also selected by Oliveira et al, as well as the histogram intersection kernel, due to the non‐linearity of the data, to increase algorithm efficiency. Likewise, Spyridonos et al made the same kernel selection, but for the detection of AK among healthy skin, attaining sensitivity and specificity values in the range of 63.7%‐80.2% and 65.6%‐82.3%, respectively.…”

Segmentation of Melanoma Skin Lesion Using Perceptual Color Difference Saliency with Morphological Analysis

“…Edge based methods such as zero-crossing of Laplacian-of-Gaussian [27] and geodesic active contour [28] are aimed at detecting discontinuities in image pixel intensity values [29]. Pixel based methods group similar pixels as belonging to a homogenous cluster that corresponds to an object or part of an object [30] and are widely applied because of their inherent simplicity and robustness [31,32]. Thresholding and clustering algorithms are archetypes of the pixel based methods that have been applied for segmentation of skin lesion [9,33].…”

Pattern Recognition in Macroscopic and Dermoscopic Images for Skin Lesion Diagnosis

“…The macroscopic image database have a total of 408 images, which were collected from several databases as described in Oliveira et al [13]. A great deal of information concerning the diagnosis of the imaged lesions provided by an expert dermatologist was also available, including diagnostics on the lesions and their features (i.e., asymmetry, border, colour and texture).…”

“…A number of peaks, valleys and straight lines of the border are computed to assess the border properties by using the vector product and inflexion point descriptors from a one-dimensional border [13]. Statistical measures, i.e., average, variance and standard deviation, are computed for each colour channel of the RGB colour space to extract the colour properties.…”