Lesion border detection in dermoscopy images using dynamic programming

Skin Research and Technology

Volumn 17, Issue 1, 2011, Pages 91-100

  Abbas, Qaisar ^a,b   Celebi, M Emre ^c   Fondón García, Irene ^d   Rashid, Muhammad ^a  

^a HUAZHONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

^b BEIJING UNIVERSITY OF POSTS AND TELECOMMUNICATIONS   (China)

^c Louisiana State University   (United States)

^d School of Industrial Engineering   (Spain)

Author keywords

Artifacts removal; Border detection; Dermoscopy; Dynamic Programming; Skin cancer

Indexed keywords

ARTIFACT REMOVAL; ARTIFACTS REMOVAL; AUTOMATED METHODS; BASAL CELL CARCINOMA; BORDER DETECTION; CELL CARCINOMA; COMPUTERIZED ANALYSIS; DERMOSCOPY; DERMOSCOPY IMAGES; EDGE POINT; LEAST SQUARES METHODS; MALIGNANT MELANOMA; OPTIMAL BOUNDARY; POINT DETECTION; ROBUST METHODS; SENSITIVITY AND SPECIFICITY; SKIN CANCER; STATISTICAL MEASURES;

BLOOD VESSELS; DERMATOLOGY; ERROR DETECTION; IMAGE SEGMENTATION;

DYNAMIC PROGRAMMING;

ALGORITHM; ARTICLE; ARTIFACT REDUCTION; BASAL CELL CARCINOMA; COMPUTER ASSISTED DIAGNOSIS; DATA ANALYSIS SOFTWARE; DIAGNOSTIC ACCURACY; DIAGNOSTIC ERROR; DIAGNOSTIC TEST ACCURACY STUDY; DYSPLASTIC NEVUS; EPILUMINESCENCE MICROSCOPY; EVALUATION; HUMAN; IMAGE ANALYSIS; IMAGE PROCESSING; MELANOCYTOSIS; MELANOMA; MERKEL CELL TUMOR; PERFORMANCE; REGRESSION ANALYSIS; SEBORRHEIC KERATOSIS; SENSITIVITY AND SPECIFICITY; SKIN TUMOR;

ARTIFACTS; CARCINOMA, BASAL CELL; DATABASES, FACTUAL; DERMOSCOPY; HAIR; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; KERATOSIS, SEBORRHEIC; LENTIGO; MELANOMA; MODELS, BIOLOGICAL; NEOPLASMS; NEVUS; SENSITIVITY AND SPECIFICITY; SKIN NEOPLASMS; SOFTWARE;

EID: 78651451409     PISSN: 0909752X     EISSN: 16000846     Source Type: Journal    
DOI: 10.1111/j.1600-0846.2010.00472.x     Document Type: Article

References (28)

1. Skin cancer facts and figures, 2009. Available at: [accessed on 13 July 2009].
2. Papamichail M, Nikolaidis I, Nikolaidis N, Glava C, Lentzas I, Marmagkiolis K et al. Merkel cell carcinoma of the upper extremity: case report and an update. World J Surg Oncol 2008; 6: 1-6.
3. Schwartz RA, Lambert WC. The Merkel cell carcinoma: a 50-year retrospect. J Surg Oncol 2005; 1: 1-4.
4. Argenziano G, Soyer HP, 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 ME, Kingravi HA, Uddin B et al. A Methodological approach to the classification of dermoscopy images. Comput Med Image Graphics 2007; 31: 362-373.
6. Xu L, Jackowski M, Goshtasby A et al. segmentation of skin cancer images. Image Vision Comput 1999; 17: 65-74.
7. Celebi ME, Aslandogan YA, Stoecker WV, Iyatomi H, Oka H, Chen X. Unsupervised border detection in dermoscopy images. Skin Res Technol 2007; 13: 454-462.
8. Celebi ME, Kingravi HA, Iyatomi H, Aslandogan YA, Stoecker WV, Moss RH et al. Border detection in dermoscopy images using statistical region merging. Skin Res Technol 2008; 14: 1-7.
9. Tang J. A multi-direction GVF snake for the segmentation of skin cancer images. Pattern Recogn 2009; 42: 1172-1179.
10. Yuan X, Situ N, Zouridakis G. A narrow band graph partitioning method for skin lesion segmentation. Pattern Recogn 2009; 42: 1017-1028.
11. Li X, Aldridge B, Ballerini L, Fisher B, Rees J. Depth data improves skin lesion segmentation. Proc Med Image Comput Comput Assist Intervent Conf 2009; 12: 1100-7.
12. Celebi ME, Iyatomi H, Schaefer G, Stoecker WV. Lesion border detection in dermoscopy images. Comput Med Image Graphics 2009; 33: 148-153.
13. Fleming MG, Steger C, Zhang J. Techniques for a structural analysis of dermatoscopic imagery. Comput Med Image Graphics 1998; 22: 375-389.
14. Wighton P, Lee TK, Atkinsa MS. Dermoscopic hair disocclusion using inpainting. Proc SPIE Med Imag Conf 2008; 8: 6914.
15. Zhou H, Chen M, Gass R, Rehg JM, Ferris L, Ho J et al. Feature-preserving artifact removal from dermoscopy images. Proceedings of the SPIE medical imaging conference 2008, 6914: 69141B-69141B-9.
16. Lee TK, Ng V, Gallagher R, Coldman A, McLean D. Dullrazor: a Software approach to hair removal from images. Comput Biol Med 1997; 27: 533-543.
17. Criminisi A, Perez P, Toyama K. Object removal by exemplar-based inpainting. Proc Comp. Vision Pattern Rec 2003; 2: 721-728.
18. Amini AA, Weymouth TE, Jain RC. Using dynamic programming for solving variational problem in vision. IEEE Trans Pattern Anal Mach Intell 1990; 12: 855-867.
19. Dermatologic Image Database. Available at: /, University of Auckland, New Zealand. [Accessed on 15 June 2009].
20. Sweet MR, Adaptive and recursive median filtering. Available at: [Accessed on 22 July 2009].
21. Kovesi P, School of computer science & software engineering. The University of Western Australia. Available at: [Accessed on 12 October 2009].
22. Adelmann HG. Butterworth equations for homomorphic filtering of images. Comput Biol Med 1998; 2 28: 169-181.
23. Li Q, Zhang L, You J, Zhang D, Bhattacharya P. Dark line detection with line width extraction. 15th Proc IEEE Int Conf Image Process 2008: 621-624.
24. Criminisi A, Perez P, Toyama K. Region filling and object removal by exemplar-based image inpainting. IEEE Trans Image Process 2004; 13: 1-13.
25. Song E, Jiang L, Jin R, Zhang L, Yuan Y, Li Q. Breast mass segmentation in mammography using plane fitting and dynamic programming. Acad Radiol 2009; 16: 826-835.
26. Yuan Y, Giger ML, Li H et al. A dual-stage method for lesion segmentation on digital mammograms. Med Phys 2007; 34: 4180-4193.
27. Lankton S, Tannenbaum A. Localizing region-based active contours. IEEE Trans Image Process 2008; 17: 2029-2039.
28. Iyatomi H, Oka H, Celebi ME et al. An Improved internet-based Melanoma screening system with dermatologist-like Tumor area extraction algorithm. Comput Med Image Graphics 2008; 32: 566-579.