Unsupervised skin lesions border detection via two-dimensional image analysis

Computer Methods and Programs in Biomedicine

Volumn 104, Issue 3, 2011, Pages

  Abbas, Qaisar ^a,b   Fondón, Irene ^c   Rashid, Muhammad ^a  

^a HUAZHONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

^b Key Laboratory of Image Processing   (China)

^c School of Engineering Path of Discovery s n   (Spain)

Author keywords

Artifacts removal; Border detection; Dermoscopy; Exemplar based image inpainting; Melanoma; Region based active contour; Skin cancer

Indexed keywords

ARTIFACTS REMOVAL; BORDER DETECTION; DERMOSCOPY; IMAGE INPAINTING; MELANOMA; REGION BASED ACTIVE CONTOURS; SKIN CANCER;

BLOOD VESSELS; CURVE FITTING; DERMATOLOGY; DISEASES; IMAGE SEGMENTATION; ONCOLOGY;

ERROR DETECTION;

ARTICLE; BASAL CELL CARCINOMA; DATA BASE; EPILUMINESCENCE MICROSCOPY; FALSE POSITIVE RESULT; HUMAN; IMAGE ANALYSIS; IMAGE PROCESSING; MELANOMA; MERKEL CELL TUMOR; SKIN CANCER; SQUAMOUS CELL CARCINOMA;

CARCINOMA, BASAL CELL; CARCINOMA, MERKEL CELL; CARCINOMA, SQUAMOUS CELL; HUMANS; MELANOMA; SENSITIVITY AND SPECIFICITY; SKIN NEOPLASMS;

EID: 80655123491     PISSN: 01692607     EISSN: 18727565     Source Type: Journal    
DOI: 10.1016/j.cmpb.2010.06.016     Document Type: Article

References (44)

1. Skin cancer facts and figures, 2009. Available from: http://www.skincancer.org/Skin-Cancer-Facts.
2. Papamichail I., Nikolaidis N., Nikolaidis C., Glava I., Lentzas K., Marmagkiolis, et al. Merkel cell carcinoma of the upper extremity: case report and an update. J. Surg. Oncol. 2008, 6(32).
3. Schwartz A., Lambert W.C. The merkel cell carcinoma: a 50-year retrospect. J. Surg. Oncol. 2005, 89(1):1-4.
4. Argenziano G., Soyer H.P., Chimenti S., Talamini R., Corona R., Sera F., et al. Dermoscopy of pigmented skin lesions: results of a consensus meeting via the Internet. J. Am. Acad. Dermatol. 2003, 48(5):679-693.
5. Celebi M.E., Kingravi H.A., Uddin B., et al. A methodological approach to the classification of dermoscopy images. Comput. Med. Imaging Graph. 2007, 31:362-373.
6. Rubegni P., Burroni M., Cevenini G., et al. Digital dermoscopy. Analysis and artificial neural network for the differentiation of clinically atypical pigmented skin lesions: a retrospective study. J. Invest. Dermatol. 2002, 119:471-474.
7. Blum H., Luedtke U., Ellwanger, et al. Digital image analysis for diagnosis of cutaneous melanoma. Development of a highly effective computer algorithm based on analysis of 837 melanocytic lesions. Brit. J. Dermatol. 2004, 151:1029-1038.
8. Burroni M., Sbano P., Cevenini G., et al. Dysplastic naevus vs. in situ melanoma: digital dermoscopy analysis. Brit. J. Dermatol. 2005, 152:679-684.
9. Patwardhan S.V., Dhawan A.P., Relue P.A. Classification of melanoma using tree structured wavelet transforms. Comput. Methods Programs Biol. 2003, 72:223-239.
10. Celebi M.E., Kingravi H.A., Iyatomi H., et al. Border detection in dermoscopy images using statistical region merging. Skin Res. Technol. 2008, 14:347-353.
11. Celebi M.E., Iyatomi H., Schaefer G., Stoecker W.V. Lesion border detection in dermoscopy images. Comput. Med. Imaging Graph. 2009, 33:148-153.
12. Celebi M.E., Aslandogan Y.A., Stoecker W.V., et al. Unsupervised border detection in dermoscopy images. Skin Res. Technol. 2007, 13:454-462.
13. Tang J. A multi-direction GVF snake for the segmentation of skin cancer images. Pattern Recognit. 2009, 42(6):1172-1179.
14. Yuan X., Situ N., Zouridakis G. A narrow band graph partitioning method for skin lesion segmentation. Pattern Recognit 2009, 42(6):1017-1028.
15. Schmid P. Segmentation of digitized dermatoscopic images by two-dimensional color clustering. IEEE Trans. Med. Imaging 1999, 18:164-171.
16. Erkol B., Moss R.H., Stanley R.H., et al. Automatic lesion boundary detection in dermoscopy images using gradient vector flow snakes. Skin Res. Technol. 2005, 11:17-26.
17. Iyatomi H., Oka H., Saito M., et al. Quantitative assessment of tumor extraction from dermoscopy images and evaluation of computer-based extraction methods for automatic melanoma diagnostic system. Melanoma Res. 2006, 16:183-190.
18. Iyatomi H., Oka H., Celebi M.E., et al. An improved internet-based melanoma screening system with dermatologist-like tumor area extraction algorithm. Comput. Med. Imaging Graph. 2008, 32:566-579.
19. Gómez D.D., Butakoff C., Ersboll B.K., Stoecker W.V. Independent histogram pursuit for segmentation of skin lesions. IEEE Trans. Biomed. Eng. 2008, 55:157-161.
20. Zhou H., Schaefer G., Sadka A., Celebi M.E. Anisotropic mean shift based fuzzy C-means segmentation of dermoscopy images. IEEE J. Sel. Top. Signal. 2009, 3:26-34.
21. M. Silveira, J.C. Nascimento, J.S. Marques, comparison of segmentation methods for melanoma diagnosis in dermoscopy images, in: Proceedings of IEEE J. Sel. Top. Signal. 3 (February) (2009) 35-45.
22. Fleming M.G., Steger C., Zhang J. Techniques for a structural analysis of dermatoscopic imagery. Comput. Med. Imaging Graph. 1998, 22(5):375-389.
23. P. Wighton, T.K. Lee, M.S. Atkinsa, Dermoscopic hair disocclusion using inpainting, in: Proceedings of the SPIE Medical Imaging 2008 Conference, 6914, pp. 691427-691427-8.
24. H. Zhou, M. Chen, R. Gass, J.M. Rehg, L. Ferris, J. Ho, et al., Feature-preserving artifact removal from dermoscopy images, in: Proceedings of the SPIE Medical Imaging 2008 Conference, 6914, 69141B-69141B-9.
25. Lee T.K., Ng V., Gallagher R., Coldman A., McLean D. Dullrazor: a software approach to hair removal from images. J. Comput. Biol. Med. 1997, 27(6):533-543.
26. A. Criminisi, P. Perez, and K. Toyama Object removal by exemplar-based inpainting, in: Proceedings of Comput. Vis. Pattern Rec., Madison, WI, June, 2003, pp. 721-728.
27. Criminisi A., Perez P., Toyama K. Region filling and object removal by exemplar-based image inpainting. IEEE Trans. Image Process. 2004, 13(9).
28. Xie F.-Y., Qin S.-Y., Jiang Z.-G., Meng R.-S. PDE-based unsupervised repair of hair-occluded information in dermoscopy images of melanoma. Comput. Med. Imaging Graph. 2009, 33:275-282.
29. Dermatologic Image Database, University of Auckland, New Zealand. Available from: (accessed on 15.06.2009). http://dermnetnz.org/doctors/dermoscopy-course/.
30. Adelmann H.G. 'Butterworth equations for homomorphic filtering of images. J. Comput. Biol. Med. 1998, 2(28):169-181.
31. M.R. Sweet, adaptive and recursive median filtering. Available from: (accessed on 22.07.2009). http://www.easysw.com/~mike/gimp/despeckle.html.
32. Steger C. Unbiased detector of curvilinear structures. IEEE Trans. Pattern Anal. 1998, 20(2):113-125.
33. Q.L.L. Zhang, J. You, D. Zhang, P. Bhattacharya, Dark line detection with line width extraction, in: Proceedings of the IEEE International Conference on Image Processing (2008) 621-624.
34. Lankton S., Tannenbaum A. Localizing region-based active contours. IEEE Trans. Image Process. 2008, 17(11):2029-2039.
35. Active Contour Segmentation matlab implementation. Available from: (accessed on 26.01.2009). http://www.mathworks.com/matlabcentral/fileexchange/19567-active-contour-segmentation.
36. Yezzi J.A., Tsai A., Willsky A. A fully global approach to image segmentation via coupled curve evolution equations. J. Vis. Commun. Image Represent. 2002, 13(1):195-216.
37. Osher S., Fedkiw R. Level Set Methods and Dynamic Implicit Surfaces 2003, Cambridge Univ. Press, New York.
38. Sethian J.A. Level Set Methods and Fast Marching Methods 1999, Springer, New York. second ed.
39. Brox T., Weickert J. Level set based image segmentation with multiple regions. Pattern Recognit. 2004, 415-423.
40. Courant R., Issacson E., Rees E. On the solution of nonlinear hyperbolic differential equations by finite differences. Commun. Pure Appl. Math. 1952, 5:243-255.
41. Sussman M., Smerka P., Osher S. A Level set approach for computing solutions to incompressible two-phase flow. J. Comput. Phys. 1994, 119:146-159.
42. Gradient Vector Flow (GVF). Available from: (accessed on 3.09.2009). http://www.iacl.ece.jhu.edu/static/gvf/.
43. Graph Partitioning Active Contours (GPAC). Available from: (accessed on 3.09.2009). http://vision.ece.ucsb.edu/~lbertelli/soft_GPAC.html.
44. Color and texture segmentation (JSEG). Available from: (accessed on 3.09.2009). http://vision.ece.ucsb.edu/segmentation/jseg/.