Modified Watershed Technique and Post-Processing for Segmentation of Skin Lesions in Dermoscopy Images

Author

Hanzheng Wang
Randy Hays Moss, Missouri University of Science and TechnologyFollow
Xiaohe Chen
R. Joe Stanley, Missouri University of Science and TechnologyFollow
William V. Stoecker, Missouri University of Science and TechnologyFollow
Mehmed Emre Celebi
Joseph M. Malters
James Michael Grichnik
Ashfaq Ahmed Marghoob
Harold S. Rabinovitz
Scott W. Menzies
Thomas M. Szalapski

Abstract

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.

Recommended Citation

H. Wang and R. H. Moss and X. Chen and R. J. Stanley and W. V. Stoecker and M. E. Celebi and J. M. Malters and J. M. Grichnik and A. A. Marghoob and H. S. Rabinovitz and S. W. Menzies and T. M. Szalapski, "Modified Watershed Technique and Post-Processing for Segmentation of Skin Lesions in Dermoscopy Images," Computerized Medical Imaging and Graphics, vol. 35, no. 2, pp. 116 - 120, Elsevier, Mar 2011.

The definitive version is available at https://doi.org/10.1016/j.compmedimag.2010.09.006

Department(s)

Electrical and Computer Engineering

Second Department

Chemistry

Sponsor(s)

National Institute of Health (U.S.)

Keywords and Phrases

Border detection; Dermoscopy images; Ground truth; Malignant melanoma; Neural network classifier; Noise removal; Post processing; Segmentation; Skin lesion; Watershed segmentation; Dermatology; Imaging systems; Landforms; Neural networks; Oncology; Watersheds; Image segmentation; artificial neural network; benign skin tumor; epiluminescence microscopy; false positive result; hair; image processing; melanoma; noise; priority journal; skin tumor; vignette; Algorithms; Dermoscopy; Humans; Image Enhancement; Image Interpretation; Computer-Assisted; Pattern Recognition; Automated; Reproducibility of Results; Sensitivity and Specificity; Skin Neoplasms; Neural network; Watershed; Image processing

International Standard Serial Number (ISSN)

0895-6111

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2011 Elsevier, All rights reserved.

Publication Date

01 Mar 2011