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

An open Internet platform to distributed image processing applied to dermoscopy

Proprietary systems for dermoscopy images analysis are available to improve the diagnosis and follow-up of the pigmented skin lesions. Their performance seems comparable with that of a human expert. Progress in computer-aided classification of melanocytic lesions depends notably on judicious choices of the algorithms dedicated to the extraction of signs from the dermoscopy images and of the method which combines these signs to classify the lesions. To allow the researcher's community to benefit from their large set of elementary algorithms already available for dermoscopy, we set up a system accessible through the Internet which: allows the engineers to register their algorithms while preserving their secrecy: their programs run on their own server; lets a user to define its own sequence of image analysis and to apply it to its images: the system automatically calls the appropriate remote programs; makes possible and stimulates the synergy of worldwide researchers in order to validate algorithms of images analysis best suited to achieve the correct diagnosis and to track the malignant melanoma; makes these techniques available to the greatest number of users through the Web and thus to support a mass screening; reduces the maintenance of the system to the minimum: it requires users only an Internet browser and engineers to follow a simple widespread standardised interface for distributed programs. Various problems should be addressed: the lack of standardisation of images acquisition: algorithms based on relative colours are best suited to this system; the copyrights on images and algorithms; charging the use of remote computer resources. This system allows for an international collaborative work in the fight against the malignant melanoma by offering a conceptual and technical platform of teledermoscopy. It is intended to support synergy between the engineers and the users implied in the diagnosis and teaching of dermoscopy

Validation of segmentation techniques for digital dermoscopy.

PURPOSE: This study aims at evaluating two automatic contour detection techniques especially developed for dermoscopic images. METHODS: Twenty-five images of lesions with a fuzzy boundary have been randomly selected. Five dermatologists experienced in dermoscopy have manually drawn the border of all the lesions and repeated the procedure after two and four weeks. The ability of a dermatologist to reproduce its own results was evaluated by measuring the non-overlapping area enclosed by its three successive contours. The interobserver variability evaluated the contour accuracy when using automatic or manual drawings. The mean probability that a pixel has been misclassified was computed for every observer and automatic technique. RESULTS: Experts in dermoscopy are not able to reproduce measurements precisely and the two automatic techniques had a lower misclassification probability than those obtained by each dermatologist. CONCLUSION: This study demonstrates that a single dermatologist should not be used as a reference, and subjective validation of lesion contour is inaccurate outside an experts's group. It is argued that image processing techniques for computer-aided diagnosis must show the best compromise within such a group

Quantization of hyperspectral image manifold using probabilistic distances

International audienceA technique of spatial-spectral quantization of hyperspectral images is introduced. Thus a quantized hyperspectral image is just summarized by K spectra which represent the spatial and spectral structures of the image. The proposed technique is based on α−connected components on a region adjacency graph. The main ingredient is a dissimilarity metric. In order to choose the metric that best fit the hyperspectral data manifold, a comparison of different probabilistic dissimilarity measures is achieved