Modified watershed technique and post-processing for segmentation of skin lesions in dermoscopy images

Hanzheng Wang, Randy H Moss, Xiaohe Chen, R Joe Stanley, William V Stoecker, M Emre Celebi, Joseph M Malters, James M Grichnik, Ashfaq A Marghoob, Harold S Rabinovitz, Scott W Menzies, Thomas M Szalapski
Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society 2011, 35 (2): 116-20
In previous research, a watershed-based algorithm was shown to be useful for automatic lesion segmentation in dermoscopy images, and was tested on a set of 100 benign and malignant melanoma images with the average of three sets of dermatologist-drawn borders used as the ground truth, resulting in an overall error of 15.98%. In this study, to reduce the border detection errors, a neural network classifier was utilized to improve the first-pass watershed segmentation; a novel "edge object value (EOV) threshold" method was used to remove large light blobs near the lesion boundary; and a noise removal procedure was applied to reduce the peninsula-shaped false-positive areas. As a result, an overall error of 11.09% was achieved.

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