A review of the quantification and classification of pigmented skin lesions: from dedicated to hand-held devices

In recent years, the incidence of skin cancer caseshas risen, worldwide, mainly due to the prolonged exposure toharmful ultraviolet radiation. Concurrently, the computerassistedmedical diagnosis of skin cancer has undergone majoradvances, through an improvement in the instrument and detectiontechnology, and the development of algorithms to processthe information. Moreover, because there has been anincreased need to store medical data, for monitoring, comparativeand assisted-learning purposes, algorithms for data processingand storage have also become more efficient in handlingthe increase of data. In addition, the potential use ofcommon mobile devices to register high-resolution imagesof skin lesions has also fueled the need to create real-timeprocessing algorithms that may provide a likelihood for thedevelopment of malignancy. This last possibility allows evennon-specialists to monitor and follow-up suspected skin cancercases. In this review, we present the major steps in the preprocessing,processing and post-processing of skin lesion images,with a particular emphasis on the quantification andclassification of pigmented skin lesions. We further reviewand outline the future challenges for the creation of minimum-feature,automated and real-time algorithms for the detectionof skin cancer from images acquired via common mobiledevices

Automating the ABCD Rule for Melanoma Detection: A Survey

The ABCD rule is a simple framework that physicians, novice dermatologists and non-physicians can use to learn about the features of melanoma in its early curable stage, enhancing thereby the early detection of melanoma. Since the interpretation of the ABCD rule traits is subjective, different solutions have been proposed in literature to tackle such subjectivity and provide objective evaluations to the different traits. This paper reviews the main contributions in literature towards automating asymmetry, border irregularity, color variegation and diameter, where the different methods involved have been highlighted. This survey could serve as an essential reference for researchers interested in automating the ABCD rule

A machine learning approach to automatic detection of irregularity in skin lesion border using dermoscopic images

Skin lesion border irregularity is considered an important clinical feature for the early diagnosis of melanoma, representing the B feature in the ABCD rule. In this article we propose an automated approach for skin lesion border irregularity detection. The approach involves extracting the skin lesion from the image, detecting the skin lesion border, measuring the border irregularity, training a Convolutional Neural Network and Gaussian naive Bayes ensemble, to the automatic detection of border irregularity, which results in an objective decision on whether the skin lesion border is considered regular or irregular. The approach achieves outstanding results, obtaining an accuracy, sensitivity, specificity, and F-score of 93.6%, 100%, 92.5% and 96.1%, respectively

Smartphone applications for triaging adults with skin lesions that are suspicious for melanoma

Background Melanoma accounts for a small proportion of all skin cancer cases but is responsible for the majority of skin cancer-related deaths. Early detection and treatment can improve survival. Smartphone applications are readily accessible and potentially offer an instant risk assessment of the likelihood of malignancy, so that the right people seek further medical attention from a clinician for more detailed assessment of the lesion. There is, however, a risk that melanomas will be missed and treatment delayed if the application reassures the user that their lesion is low risk. Objectives To determine the diagnostic accuracy of smartphone applications to rule out cutaneous invasive melanoma and intraepidermal melanocytic variants in adults with concerns about suspicious skin lesions.NIH

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

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