title: Counting moles automatically from back images.
abstract: Density of moles is a strong predictor of malignant melanoma, therefore, enumeration of moles is often an integral part of many studies that look at malignant melanoma. An automatic method of segmenting and counting moles would help standardize studies, compared with manual counting. We have developed an unsupervised algorithm for segmenting and counting moles from two-dimensional color images of the back torso region, as part of a study to evaluate the effectiveness of sunscreen. The method consists of a new variant of mean shift filtering that forms clusters in the image and removes noise, a region growing procedure to select candidates, and a rule-based classifier to identify moles. When this algorithm was compared to an assessment by an expert dermatologist, the algorithm showed a sensitivity rate of 91% and diagnostic accuracy of 90% on the test set, for moles larger than 1.5 mm in diameter.
authors.affiliation: School of Computing Science, Simon Fraser University, Burnaby, BC, Canada. tlee@bccancer.bc.ca
authors.forename: Tim K
authors.initials: TK
authors.lastname: Lee
authors.forename: M Stella
authors.initials: MS
authors.lastname: Atkins
authors.forename: Michael A
authors.initials: MA
authors.lastname: King
authors.forename: Savio
authors.initials: S
authors.lastname: Lau
authors.forename: David I
authors.initials: DI
authors.lastname: McLean
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001415:Back / Q000473:pathology*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D009508:Nevus, Pigmented / Q000473:pathology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012878:Skin Neoplasms / Q000473:pathology*