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

Nondermatoscopic Digital Imaging of Pigmented Lesions

Background/aims: Pigmented lesions are often difficult to evaluate clinically. Improvement of diagnostic accuracy by dermatoscopy has attracted much interet. With advanced digital imaging measurement of assymmetry, border irregularity and relative color as well as texture characteristics, lesional depth and changes in lesional area are now possible, the object of this review is to conclude the present status of these techniques and their potential. Conclusions: Digital imaging of pigmented lesions to this date include acquiring and storing of images, quantification of clinical features including asymmetry, and teledermatology with transfer of images. Predicted uses include malignancy evaluation, delineation of depth of invasion and the development of large collections of pigment lesions observations. The field is rapidly expanding. As of 1994, it is unknown what role digital imaging will ultimately play in clinical dermatology

Detection of Asymmetric Blotches (asymmetric Structureless Areas) in Dermoscopy Images of Malignant Melanoma using Relative Color

Dermoscopy, also known as dermatoscopy or epiluminescence microscopy (ELM), is a non-invasive, in vivo technique, which permits visualization of features of pigmented melanocytic neoplasms that are not discernable by examination with the naked eye. One prominent feature useful for melanoma detection in dermoscopy images is the asymmetric blotch (asymmetric structureless area)

Fuzzy logic techniques for blotch feature evaluation in dermoscopy images

Blotches, also called structureless areas, are critical in differentiating malignant melanoma from benign lesions in dermoscopy skin lesion images. In this paper, fuzzy logic techniques are investigated for the automatic detection of blotch features for malignant melanoma discrimination. Four fuzzy sets representative of blotch size and relative and absolute blotch colors are used to extract blotchy areas from a set of dermoscopy skin lesion images. Five previously reported blotch features are computed from the extracted blotches as well as four new features. Using a neural network classifier, malignant melanoma discrimination results are optimized over the range of possible alpha-cuts and compared with results using crisp blotch features. Features computed from blotches using the fuzzy logic techniques based on three plane relative Color and blotch size yield the highest diagnostic accuracy of 81.2%. (C) 2008 Elsevier Ltd. All rights reserved