title: Automatic skin lesion segmentation via iterative stochastic region merging.
abstract: An automatic method for segmenting skin lesions in conventional macroscopic images is presented. The images are acquired with conventional cameras, without the use of a dermoscope. Automatic segmentation of skin lesions from macroscopic images is a very challenging problem due to factors such as illumination variations, irregular structural and color variations, the presence of hair, as well as the occurrence of multiple unhealthy skin regions. To address these factors, a novel iterative stochastic region-merging approach is employed to segment the regions corresponding to skin lesions from the macroscopic images, where stochastic region merging is initialized first on a pixel level, and subsequently on a region level until convergence. A region merging likelihood function based on the regional statistics is introduced to determine the merger of regions in a stochastic manner. Experimental results show that the proposed system achieves overall segmentation error of under 10% for skin lesions in macroscopic images, which is lower than that achieved by existing methods.
authors.affiliation: Department of Systems Design Engineering, University of Waterloo, Waterloo, Canada. a28wong@uwaterloo.ca
authors.forename: Alexander
authors.initials: A
authors.lastname: Wong
authors.forename: Jacob
authors.initials: J
authors.lastname: Scharcanski
authors.forename: Paul
authors.initials: P
authors.lastname: Fieguth
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D008545:Melanoma / Q000175:diagnosis*; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D012878:Skin Neoplasms / Q000175:diagnosis*; D012880:Skin Pigmentation; D013269:Stochastic Processes