Melanoma and other skin lesion detection using smart handheld devices.

Smartphones of the latest generation featuring advanced multicore processors, dedicated microchips for graphics, high-resolution cameras, and innovative operating systems provide a portable platform for running sophisticated medical screening software and delivering point-of-care patient diagnostic services at a very low cost. In this chapter, we present a smartphone digital dermoscopy application that can analyze high-resolution images of skin lesions and provide the user with feedback about the likelihood of malignancy. The same basic procedure has been adapted to evaluate other skin lesions, such as the flesh-eating bacterial disease known as Buruli ulcer.

Implementation of the 7-point checklist for melanoma detection on smart handheld devices.

In this paper we implement the 7-point checklist, a set of dermoscopic criteria widely used by clinicians for melanoma detection, on smart handheld devices, such as the Apple iPhone and iPad. The application developed is using sophisticated image processing and pattern recognition algorithms, yet it is light enough to run on a handheld device with limited memory and computational speed. When combined with a commercially available handheld dermoscope that provides proper lesion illumination, this application provides a truly self-contained handheld system for melanoma detection.

EVALUATING SAMPLING STRATEGIES OF DERMOSCOPIC INTEREST POINTS.

Among the most critical components of a computerized system for automated melanoma detection is image sampling and pooling of the extracted features. In this paper, we propose a new method for sampling and pooling based on a combination of spatial pooling and graph theory features. The performance of the new method is evaluated using a dataset of more than 1,500 images representing pigmented skin lesions of known pathology.

SkinScan©: A PORTABLE LIBRARY FOR MELANOMA DETECTION ON HANDHELD DEVICES.

We have developed a portable library for automated detection of melanoma termed SkinScan© that can be used on smartphones and other handheld devices. Compared to desktop computers, embedded processors have limited processing speed, memory, and power, but they have the advantage of portability and low cost. In this study we explored the feasibility of running a sophisticated application for automated skin cancer detection on an Apple iPhone 4.

Modeling spatial relation in skin lesion images by the graph walk kernel.

Early skin cancer detection with the help of dermoscopic images is becoming more and more important. Previous methods generally ignored the spatial relation of the pixels or regions inside the lesion. We propose to employ a graph representation of the skin lesion to model the spatial relation.