Open AccessWatershed segmentation of dermoscopy images using a watershed technique
Author(s) -
Wang Hanzheng,
Chen Xiaohe,
Moss Randy H.,
Stanley R. Joe,
Stoecker William V.,
Celebi M. Emre,
Szalapski Thomas M.,
Malters Joseph M.,
Grichnik James M.,
Marghoob Ashfaq A.,
Rabinovitz Harold S.,
Menzies Scott W.
Publication year - 2010
Publication title -
skin research and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.521
H-Index - 69
eISSN - 1600-0846
pISSN - 0909-752X
DOI - 10.1111/j.1600-0846.2010.00445.x
Subject(s) - watershed , segmentation , artificial intelligence , computer science , image segmentation , computer vision , smoothing , preprocessor , minimum bounding box , pattern recognition (psychology) , image (mathematics)
Background/purpose: Automatic lesion segmentation is an important part of computer‐based image analysis of pigmented skin lesions. In this research, a watershed algorithm is developed and investigated for adequacy of skin lesion segmentation in dermoscopy images. Methods: Hair, black border and vignette removal methods are introduced as preprocessing steps. The flooding variant of the watershed segmentation algorithm was implemented with novel features adapted to this domain. An outer bounding box, determined by a difference function derived from horizontal and vertical projection functions, is added to estimate the lesion area, and the lesion area error is reduced by a linear estimation function. As a post‐processing step, a second‐order B‐Spline smoothing method is introduced to smooth the watershed border. Results: Using the average of three sets of dermatologist‐drawn borders as the ground truth, an overall error of 15.98% was obtained using the watershed technique. Conclusion: The implementation of the flooding variant of the watershed algorithm presented here allows satisfactory automatic segmentation of pigmented skin lesions.