Efficient segmentation of large-area skin images: an overview of image processing

Background/aims: This paper is the first of a series describing research and development performed by the SPOTS project towards automating the process of monitoring pigmented lesions for change over time. A comprehensive overview of digital image processing techniques used for the systematic approach to segmentation of large-area skin images, including the description and classification of skin features, is presented.

Methods: The multiresolution hierarchical segmentation technique, which makes use of image pyramid data structures and cooperative computation with unforced linking, was used to group pixels with similar color properties. This technique was further enhanced to enforce contiguity of segments by determining connected components. The resulting segmentation was then classified by using representative properties of pyramid nodes as input to a cascade of multilayer perceptron neural networks trained using the classical backpropagation algorithm. In order to make our system more resistant to imaging distortions, a technique was developed to grow regions based upon the resulting classification.

Conclusions: This system produces a segmentation at multiple resolutions based upon color, where each segment is a contiguous group of pixels on the image. The classification makes use of both spatial and color properties, as well as parent-child relationships within the pyramid. The addition of classification-driven region growing reduces the change in lesion area where the boundary fades into surrounding skin. Furthermore, the system contains parameters that can be tuned to provide desirable results.