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

Computational Diagnosis of Skin Lesions from Dermoscopic Images using Combined Features

There has been an alarming increase in the number of skin cancer cases worldwide in recent years, which has raised interest in computational systems for automatic diagnosis to assist early diagnosis and prevention. Feature extraction to describe skin lesions is a challenging research area due to the difficulty in selecting meaningful features. The main objective of this work is to find the best combination of features, based on shape properties, colour variation and texture analysis, to be extracted using various feature extraction methods. Several colour spaces are used for the extraction of both colour- and texture-related features. Different categories of classifiers were adopted to evaluate the proposed feature extraction step, and several feature selection algorithms were compared for the classification of skin lesions. The developed skin lesion computational diagnosis system was applied to a set of 1104 dermoscopic images using a cross-validation procedure. The best results were obtained by an optimum-path forest classifier with very promising results. The proposed system achieved an accuracy of 92.3%, sensitivity of 87.5% and specificity of 97.1% when the full set of features was used. Furthermore, it achieved an accuracy of 91.6%, sensitivity of 87% and specificity of 96.2%, when 50 features were selected using a correlation-based feature selection algorithm

Computational Methods for Pigmented Skin Lesion Classification in Images: Review and Future Trends

Skin cancer is considered as one of the most common types of cancer in several countries, and its incidence rate has increased in recent years. Melanoma cases have caused an increasing number of deaths worldwide, since this type of skin cancer is the most aggressive compared to other types. Computational methods have been developed to assist dermatologists in early diagnosis of skin cancer. An overview of the main and current computational methods that have been proposed for pattern analysis and pigmented skin lesion classification is addressed in this review. In addition, a discussion about the application of such methods, as well as future trends, is also provided. Several methods for feature extraction from both macroscopic and dermoscopic images and models for feature selection are introduced and discussed. Furthermore, classification algorithms and evaluation procedures are described, and performance results for lesion classification and pattern analysis are given

A computational approach for detecting pigmented skin lesions in macroscopic images

Skin cancer is considered one of the most common types of cancer in several countries and its incidencerate has increased in recent years. Computational methods have been developed to assist dermatologistsin early diagnosis of skin cancer. Computational analysis of skin lesion images has become a challengingresearch area due to the difficulty in discerning some types of skin lesions. A novel computational approachis presented for extracting skin lesion features from images based on asymmetry, border, colourand texture analysis, in order to diagnose skin lesion types. The approach is based on an anisotropic diffusionfilter, an active contour model without edges and a support vector machine. Experiments wereperformed regarding the segmentation and classification of pigmented skin lesions in macroscopic images,with the results obtained being very promising

Diagnosis of skin cancer using novel computer vision and deep learning techniques

Recent years have noticed an increase in the total number of skin cancer cases and it is projected to grow exponentially, however mortality rate of malignant melanoma can be decreased if it is diagnosed and treated in its early stage. Notwithstanding the fact that visual similarity between benign and malignant lesions makes the task of diagnosis difficult even for an expert dermatologist, thereby increasing the chances of false prediction. This dissertation proposes two novel methods of computer-aided diagnosis for the classification of malignant lesion. The first method pre-processes the acquired image by the Dull razor method (for digital hair removal) and histogram equalisation. Henceforth the image is segmented by the proposed method using LR-fuzzy logic and it achieves an accuracy, sensitivity and specificity of 96.50%, 97.50% and 96.25% for the PH2 dataset; 96.16%, 91.88% and 98.26% for the ISIC 2017 dataset; 95.91%, 91.62% and 97.37% for ISIC 2018 dataset respectively. Furthermore, the image is classified by the modified You Only Look Once (YOLO v3) classifier and it yields an accuracy, sensitivity and specificity of 98.16%, 95.43%, and 99.50% respectively. The second method enhances the images by removing digital artefacts and histogram equalisation. Thereafter, triangular neutrosophic number (TNN) is used for segmentation of lesion, which achieves an accuracy, sensitivity, and specificity of 99.00%, 97.50%, 99.38% for PH2; 98.83%, 98.48%, 99.01% for ISIC 2017; 98.56%, 98.50%, 98.58% for ISIC 2018; and 97.86%, 97.56%, 97.97% for ISIC 2019 dataset respectively. Furthermore, data augmentation is performed by the addition of artefacts and noise to the training dataset and rotating the images at an angle of 650, 1350, and 2150 such that the training dataset is increased to 92838 from 30946 images. Additionally, a novel classifier based on inception and residual module is trained over augmented dataset and it is able to achieve an accuracy, sensitivity and specificity of 99.50%, 100%, 99.38% for PH2; 99.33%, 98.48%, 99.75% for ISIC 2017; 98.56%, 97.61%, 98.88% for ISIC 2018 and 98.04%, 96.67%, 98.52% for ISIC 2019 dataset respectively. Later in our dissertation, the proposed methods are deployed into real-time mobile applications, therefore enabling the users to diagnose the suspected lesion with ease and accuracy

Characterising pattern asymmetry in pigmented skin lesions

Abstract. In clinical diagnosis of pigmented skin lesions asymmetric pigmentation is often indicative of melanoma. This paper describes a method and measures for characterizing lesion symmetry. The estimate of mirror symmetry is computed first for a number of axes at different degrees of rotation with respect to the lesion centre. The statistics of these estimates are the used to assess the overall symmetry. The method is applied to three different lesion representations showing the overall pigmentation, the pigmentation pattern, and the pattern of dermal melanin. The best measure is a 100% sensitive and 96% specific indicator of melanoma on a test set of 33 lesions, with a separate training set consisting of 66 lesions