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

doi:10.1016/j.compmedimag.2010.09.006

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

Research output: Contribution to journal › Article › peer-review

57 Scopus citations

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.

Original language	English (US)
Pages (from-to)	116-120
Number of pages	5
Journal	Computerized Medical Imaging and Graphics
Volume	35
Issue number	2
DOIs	https://doi.org/10.1016/j.compmedimag.2010.09.006
State	Published - Mar 2011
Externally published	Yes

Keywords

Image processing
Malignant melanoma
Neural network
Segmentation
Watershed

ASJC Scopus subject areas

Radiological and Ultrasound Technology
Radiology Nuclear Medicine and imaging
Computer Vision and Pattern Recognition
Health Informatics
Computer Graphics and Computer-Aided Design

Access to Document

10.1016/j.compmedimag.2010.09.006

Cite this

Wang, H., Moss, R. H., Chen, X., Stanley, R. J., Stoecker, W. V., Celebi, M. E., Malters, J. M., Grichnik, J. M., Marghoob, A. A., Rabinovitz, H. S., Menzies, S. W., & Szalapski, T. M. (2011). Modified watershed technique and post-processing for segmentation of skin lesions in dermoscopy images. Computerized Medical Imaging and Graphics, 35(2), 116-120. https://doi.org/10.1016/j.compmedimag.2010.09.006

Wang, H, Moss, RH, Chen, X, Stanley, RJ, Stoecker, WV, Celebi, ME, Malters, JM, Grichnik, JM, Marghoob, AA, Rabinovitz, HS, Menzies, SW & Szalapski, TM 2011, '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. https://doi.org/10.1016/j.compmedimag.2010.09.006

@article{d6d4462a55ba4b8da50d8909c524df8b,

title = "Modified watershed technique and post-processing for segmentation of skin lesions in dermoscopy images",

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.",

keywords = "Image processing, Malignant melanoma, Neural network, Segmentation, Watershed",

author = "Hanzheng Wang and Moss, {Randy H.} and Xiaohe Chen and Stanley, {R. Joe} and Stoecker, {William V.} and Celebi, {M. Emre} and Malters, {Joseph M.} and Grichnik, {James M.} and Marghoob, {Ashfaq A.} and Rabinovitz, {Harold S.} and Menzies, {Scott W.} and Szalapski, {Thomas M.}",

note = "Funding Information: This publication was made possible by Grant number SBIR R44 CA-101639-02A2 of the National Institutes of Health (NIH) . Its contents are solely the responsibility of the authors and do not necessarily represent the official views of NIH. We gratefully acknowledge the contributions of Raeed Chowdhury and Mark Wronkiewicz in writing the black border removal software.",

year = "2011",

month = mar,

doi = "10.1016/j.compmedimag.2010.09.006",

language = "English (US)",

volume = "35",

pages = "116--120",

journal = "Computerized Medical Imaging and Graphics",

issn = "0895-6111",

publisher = "Elsevier Limited",

number = "2",

}

TY - JOUR

T1 - Modified watershed technique and post-processing for segmentation of skin lesions in dermoscopy images

AU - Wang, Hanzheng

AU - Moss, Randy H.

AU - Chen, Xiaohe

AU - Stanley, R. Joe

AU - Stoecker, William V.

AU - Celebi, M. Emre

AU - Malters, Joseph M.

AU - Grichnik, James M.

AU - Marghoob, Ashfaq A.

AU - Rabinovitz, Harold S.

AU - Menzies, Scott W.

AU - Szalapski, Thomas M.

N1 - Funding Information: This publication was made possible by Grant number SBIR R44 CA-101639-02A2 of the National Institutes of Health (NIH) . Its contents are solely the responsibility of the authors and do not necessarily represent the official views of NIH. We gratefully acknowledge the contributions of Raeed Chowdhury and Mark Wronkiewicz in writing the black border removal software.

PY - 2011/3

Y1 - 2011/3

N2 - 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.

AB - 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.

KW - Image processing

KW - Malignant melanoma

KW - Neural network

KW - Segmentation

KW - Watershed

UR - http://www.scopus.com/inward/record.url?scp=79951515493&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=79951515493&partnerID=8YFLogxK

U2 - 10.1016/j.compmedimag.2010.09.006

DO - 10.1016/j.compmedimag.2010.09.006

M3 - Article

C2 - 20970307

AN - SCOPUS:79951515493

SN - 0895-6111

VL - 35

SP - 116

EP - 120

JO - Computerized Medical Imaging and Graphics

JF - Computerized Medical Imaging and Graphics

IS - 2

ER -

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

Abstract

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this