Exemplar-based segmentation of pigmented skin lesions from dermoscopy images

Authors:
Howard Zhou;James M. Rehg;Mei Chen
Affiliations:
School of Interactive Computing, GVU Center, Georgia Institute of Technology;School of Interactive Computing, GVU Center, Georgia Institute of Technology;Intel Labs Pittsburgh
Venue:
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Year:
2010

Citing 1
Cited 2

k-means++: the advantages of careful seeding

SODA '07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms

Skin melanoma segmentation by morphological approach

Proceedings of the International Conference on Advances in Computing, Communications and Informatics
Methodological review: Computerized analysis of pigmented skin lesions: A review

Artificial Intelligence in Medicine

Quantified Score

Hi-index	0.00

Visualization

Abstract

Automated segmentation of pigmented skin lesions (PSLs) from dennoscopy images is an important step for computer-aided diagnosis of skin cancer. The segmentation task involves classifying each image pixel as either lesion or skin. It is challenging because lesion and skin can often have similar appearance. We present a novel exemplar-based algorithm for lesion segmentation which leverages the context provided by a global color model to retrieve annotated examples which are most similar to a given query image. Pixel labels are generated through a probabilistic voting rule and smoothed using a dermoscopy-specific spatial prior. We compare our method to three competing techniques using a large dataset of dermoscopy images with hand-segmented ground truth, We show that our exemplar-based approach yields significantly better segmentations and is computationally efficient.