Skin Lesion Segmentation Using Color Channel Optimization and Clustering-based Histogram Thresholding
Automatic segmentation of skin lesions is the first step
towards the automated analysis of malignant melanoma. Although
numerous segmentation methods have been developed, few studies
have focused on determining the most effective color space for
melanoma application. This paper proposes an automatic segmentation
algorithm based on color space analysis and clustering-based histogram
thresholding, a process which is able to determine the optimal
color channel for detecting the borders in dermoscopy images. The
algorithm is tested on a set of 30 high resolution dermoscopy images.
A comprehensive evaluation of the results is provided, where borders
manually drawn by four dermatologists, are compared to automated
borders detected by the proposed algorithm, applying three previously
used metrics of accuracy, sensitivity, and specificity and a new metric
of similarity. By performing ROC analysis and ranking the metrics,
it is demonstrated that the best results are obtained with the X and
XoYoR color channels, resulting in an accuracy of approximately
97%. The proposed method is also compared with two state-of-theart
skin lesion segmentation methods.
[1] G. Argenziano, H. P. Soyer, S. Chimenti, and R. T. et al., "Dermoscopy
of pigmented skin lesions: Results of a consensus meeting via the
Internet," Journal of the American Academy of Dermatology, vol. 48,
pp. 679-693, 2003.
[2] P. Braun, H. Rabinovitz, M. Oliviero, A. Kopf, and J. Saurat, "Dermoscopy
of pigmented lesions," Journal of the American Academy of
Dermatology, vol. 52, no. 1, pp. 109-121, 2005.
[3] A. Perrinaud, O. Gaide, L. French, J.-H. Saurat, A. Marghoob, and
R. Braun, "Can automated dermoscopy image analysis instruments
provide added benefit for the dermatologist? A study comparing the
results of three systems," British Journal of Dermatology, vol. 157, pp.
926-933, 2007.
[4] M. E. Celebi, H. Iyatomi, G. Schaefer, and W. V. Stoecker, "Lesion border
detection in dermoscopy images," Computerized Medical Imaging
and Graphics, vol. 33, no. 2, pp. 148-153, 2009.
[5] H. Iyatomi, H. Oka, M. E. Celebi, M. Hashimoto, M. Hagiwara,
M. Tanaka, and K. Ogawa, "An improved internet-based melanoma
screening system with dermatologist-like tumor area extraction algorithm,"
Computerized Medical Imaging and Graphics, vol. 32, no. 7,
pp. 566-579, 2008.
[6] M. Hintz-Madsen, L. K. Hansen, J. Larsen, and K. T. Drzewiecki, "A
probabilistic neural network framework for the detection of malignant
melanoma." Artificial neural networks in cancer diagnosis. Prognosis
and Patient Management, pp. 141-183, 2001.
[7] R. Melli, C. Grana, and R. Cucchiara, "Comparison of color clustering
algorithms for segmentation of dermatological images," in SPIE Medical
Imaging, vol. 6144, 2006, pp. 3S1-3S9.
[8] G. Hance, S. Umbaugh, R. Moss, and W. V. Stoecker, "Unsupervised
color image segmentation:With application to skin tumor borders," IEEE
Engineering in Medicine and Biology Magazine, vol. 15, pp. 104-111,
1996.
[9] P. Schmid, "Segmentation of digitized dermatoscopic images by twodimensional
color clustering," IEEE Transactions on Medical Imaging,
vol. 18, no. 2, pp. 164-171, 1999.
[10] M. E. Celebi, Y. A. Aslandogan, W. V. Stoecker, H. Iyatomi, H. Oka,
and X. Chen, "Unsupervised border detection in dermoscopy images,"
Skin Research and Technology, vol. 13, pp. 454-462, 2007.
[11] M. E. Celebi, H. A. Kingravi, H. Iyatomi, Y. A. Aslandogan, W. V.
Stoecker, R. H. Moss, J. M. Malters, J. M. Grichnik, A. A. Marghoob,
H. S. Rabinovitz, and S. W. Menzies, "Border detection in dermoscopy
images using statistical region merging," Skin Research and Technology,
vol. 14, pp. 347-353, 2008.
[12] T. Lee, , V. Ng, R. Gallagher, A. Coldman, and D. McLean, "Dullrazor:
A software approach to hair removal from images," Computers in
Biology and Medicine, vol. 27, pp. 533-543, 1997.
[13] L. Lucchese and S. Mitra, "Color image segmentation: A state-of-theart
survey," in Proceedings of Indian National Science Academy Part A,
PINSA2001, 2001, pp. 207-221.
[14] K. N. Plataniotis and A. N. Venetsanopoulos, Color Image Processing
and Applications. Springer, 2000.
[15] N. Otsu, "A threshold selection method from gray-level histograms,"
IEEE Transactions on Systems, Man, and Cybernetics, vol. 9, no. 1, pp.
62-66, 1979.
[16] R. M. Haralick and L. G. Shapiro, Computer and Robot Vision.
Addison-Wesley, 1992, vol. 1.
[17] M. E. Celebi, G. Schaefer, H. Iyatomi, W. V. Stoecker, J. M. Malters,
and J. M. Grichnik, "An improved objective evaluation measure for
border detection in dermoscopy images," to appear in Skin Research
and Technology.
[18] T. Sorensen, "A method of establishing groups of equal amplitude in
plant sociology based on similarity of species and its application to
analyses of the vegetation on danish commons." Royal Danish Academy
of Sciences and Letters, vol. 5, pp. 1-34, 1948.
[19] J. Davis and M. Goadrich, "The relationship between precision-recall
and roc curves," in Proceeding of 23rd International Conference on
Machine Learning (ICML), vol. 148, 2006, pp. 233-240.
[1] G. Argenziano, H. P. Soyer, S. Chimenti, and R. T. et al., "Dermoscopy
of pigmented skin lesions: Results of a consensus meeting via the
Internet," Journal of the American Academy of Dermatology, vol. 48,
pp. 679-693, 2003.
[2] P. Braun, H. Rabinovitz, M. Oliviero, A. Kopf, and J. Saurat, "Dermoscopy
of pigmented lesions," Journal of the American Academy of
Dermatology, vol. 52, no. 1, pp. 109-121, 2005.
[3] A. Perrinaud, O. Gaide, L. French, J.-H. Saurat, A. Marghoob, and
R. Braun, "Can automated dermoscopy image analysis instruments
provide added benefit for the dermatologist? A study comparing the
results of three systems," British Journal of Dermatology, vol. 157, pp.
926-933, 2007.
[4] M. E. Celebi, H. Iyatomi, G. Schaefer, and W. V. Stoecker, "Lesion border
detection in dermoscopy images," Computerized Medical Imaging
and Graphics, vol. 33, no. 2, pp. 148-153, 2009.
[5] H. Iyatomi, H. Oka, M. E. Celebi, M. Hashimoto, M. Hagiwara,
M. Tanaka, and K. Ogawa, "An improved internet-based melanoma
screening system with dermatologist-like tumor area extraction algorithm,"
Computerized Medical Imaging and Graphics, vol. 32, no. 7,
pp. 566-579, 2008.
[6] M. Hintz-Madsen, L. K. Hansen, J. Larsen, and K. T. Drzewiecki, "A
probabilistic neural network framework for the detection of malignant
melanoma." Artificial neural networks in cancer diagnosis. Prognosis
and Patient Management, pp. 141-183, 2001.
[7] R. Melli, C. Grana, and R. Cucchiara, "Comparison of color clustering
algorithms for segmentation of dermatological images," in SPIE Medical
Imaging, vol. 6144, 2006, pp. 3S1-3S9.
[8] G. Hance, S. Umbaugh, R. Moss, and W. V. Stoecker, "Unsupervised
color image segmentation:With application to skin tumor borders," IEEE
Engineering in Medicine and Biology Magazine, vol. 15, pp. 104-111,
1996.
[9] P. Schmid, "Segmentation of digitized dermatoscopic images by twodimensional
color clustering," IEEE Transactions on Medical Imaging,
vol. 18, no. 2, pp. 164-171, 1999.
[10] M. E. Celebi, Y. A. Aslandogan, W. V. Stoecker, H. Iyatomi, H. Oka,
and X. Chen, "Unsupervised border detection in dermoscopy images,"
Skin Research and Technology, vol. 13, pp. 454-462, 2007.
[11] M. E. Celebi, H. A. Kingravi, H. Iyatomi, Y. A. Aslandogan, W. V.
Stoecker, R. H. Moss, J. M. Malters, J. M. Grichnik, A. A. Marghoob,
H. S. Rabinovitz, and S. W. Menzies, "Border detection in dermoscopy
images using statistical region merging," Skin Research and Technology,
vol. 14, pp. 347-353, 2008.
[12] T. Lee, , V. Ng, R. Gallagher, A. Coldman, and D. McLean, "Dullrazor:
A software approach to hair removal from images," Computers in
Biology and Medicine, vol. 27, pp. 533-543, 1997.
[13] L. Lucchese and S. Mitra, "Color image segmentation: A state-of-theart
survey," in Proceedings of Indian National Science Academy Part A,
PINSA2001, 2001, pp. 207-221.
[14] K. N. Plataniotis and A. N. Venetsanopoulos, Color Image Processing
and Applications. Springer, 2000.
[15] N. Otsu, "A threshold selection method from gray-level histograms,"
IEEE Transactions on Systems, Man, and Cybernetics, vol. 9, no. 1, pp.
62-66, 1979.
[16] R. M. Haralick and L. G. Shapiro, Computer and Robot Vision.
Addison-Wesley, 1992, vol. 1.
[17] M. E. Celebi, G. Schaefer, H. Iyatomi, W. V. Stoecker, J. M. Malters,
and J. M. Grichnik, "An improved objective evaluation measure for
border detection in dermoscopy images," to appear in Skin Research
and Technology.
[18] T. Sorensen, "A method of establishing groups of equal amplitude in
plant sociology based on similarity of species and its application to
analyses of the vegetation on danish commons." Royal Danish Academy
of Sciences and Letters, vol. 5, pp. 1-34, 1948.
[19] J. Davis and M. Goadrich, "The relationship between precision-recall
and roc curves," in Proceeding of 23rd International Conference on
Machine Learning (ICML), vol. 148, 2006, pp. 233-240.
@article{"International Journal of Medical, Medicine and Health Sciences:58504", author = "Rahil Garnavi and Mohammad Aldeen and M. Emre Celebi and Alauddin Bhuiyan and Constantinos Dolianitis and George Varigos", title = "Skin Lesion Segmentation Using Color Channel Optimization and Clustering-based Histogram Thresholding", abstract = "Automatic segmentation of skin lesions is the first step
towards the automated analysis of malignant melanoma. Although
numerous segmentation methods have been developed, few studies
have focused on determining the most effective color space for
melanoma application. This paper proposes an automatic segmentation
algorithm based on color space analysis and clustering-based histogram
thresholding, a process which is able to determine the optimal
color channel for detecting the borders in dermoscopy images. The
algorithm is tested on a set of 30 high resolution dermoscopy images.
A comprehensive evaluation of the results is provided, where borders
manually drawn by four dermatologists, are compared to automated
borders detected by the proposed algorithm, applying three previously
used metrics of accuracy, sensitivity, and specificity and a new metric
of similarity. By performing ROC analysis and ranking the metrics,
it is demonstrated that the best results are obtained with the X and
XoYoR color channels, resulting in an accuracy of approximately
97%. The proposed method is also compared with two state-of-theart
skin lesion segmentation methods.", keywords = "Border detection, Color space analysis, Dermoscopy,Histogram thresholding, Melanoma, Segmentation.", volume = "3", number = "12", pages = "364-9", }
