A fully automatic random Walker segmentation for skin lesions in a supervised setting.
We present a method for automatically segmenting skin lesions by initializing the random walker algorithm with seed points whose properties, such as colour and texture, have been learnt via a training set. We leverage the speed and robustness of the random walker algorithm and augment it into a fully automatic method by using supervised statistical pattern recognition techniques. We validate our results by comparing the resulting segmentations to the manual segmentations of an expert over 120 cases, including 100 cases which are categorized as difficult (i.e.: low contrast, heavily occluded, etc.). We achieve an F-measure of 0.95 when segmenting easy cases, and an F-measure of 0.85 when segmenting difficult cases.
Wighton
Paul
P
School of Computing Science, Simon Fraser University, Canada. pwighton@sfu.ca
Sadeghi
Maryam
M
Lee
Tim K
TK
Atkins
M Stella
MS
D000465:Algorithms; D001185:Artificial Intelligence; D003124:Colorimetry / Q000379:methods*; D003627:Data Interpretation, Statistical; D046169:Dermoscopy / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012867:Skin / Q000473:pathology*; D012871:Skin Diseases / Q000473:pathology*