Border detection in dermoscopy images using statistical region merging

Skin Research and Technology

Volumn 14, Issue 3, 2008, Pages 347-353

  Celebi, M Emre ^a   Kingravi, Hassan A ^b   Iyatomi, Hitoshi ^c   Aslandogan, Y Alp ^d   Stoecker, William V ^e   Moss, Randy H ^f   Malters, Joseph M ^g   Grichnik, James M ^h   Marghoob, Ashfaq A ⁱ   Rabinovitz, Harold S ^j   Menzies, Scott W ^a  

^a LOUISIANA STATE UNIVERSITY   (United States)

^b TEXAS A AND M UNIVERSITY   (United States)

^c HOSEI UNIVERSITY   (Japan)

^d PRAIRIE VIEW A AND M UNIVERSITY   (United States)

^e University of Missouri Rolla ^*

^f Missouri University of Science and Technology   (United States)

^g DUKE UNIVERSITY MEDICAL CENTER   (United States)

^h MEMORIAL SLOAN KETTERING CANCER CENTER   (United States)

ⁱ SKIN AND CANCER ASSOCIATES   (United States)

^j Sydney Melanoma Unit   (Australia)

more..

Author keywords

Border detection; Computer aided diagnosis; Dermoscopy; Melanoma; Segmentation; Skin cancer; Statistical region merging

Indexed keywords

ARSENIC COMPOUNDS; BOOLEAN FUNCTIONS; CLUSTERING ALGORITHMS; COMPUTER AIDED DIAGNOSIS; COMPUTER GRAPHICS; ERROR DETECTION; FUZZY CLUSTERING; HEALTH; IMAGE ENHANCEMENT; IMAGE PROCESSING; IMAGING SYSTEMS; MEDICAL IMAGING; MERGING; OPTICAL DATA PROCESSING; SKIN; STATISTICAL METHODS;

AUTOMATED METHODS; BORDER DETECTION; COMPUTER-AIDED DIAGNOSIS (CAD); DERMOSCOPY IMAGES; EXTRACTION ALGORITHMS; FUZZY C MEANS (FCM); IMAGE PROCESSING TECHNIQUES; MEAN SHIFT CLUSTERING; MELANOMA (MM); PIGMENTED SKIN LESIONS; REGION MERGING; SKIN IMAGING TECHNOLOGY;

IMAGING TECHNIQUES;

ARTICLE; AUTOMATION; BENIGN SKIN TUMOR; COMPUTER ASSISTED DIAGNOSIS; DIAGNOSTIC ACCURACY; DIAGNOSTIC ERROR; EPILUMINESCENCE MICROSCOPY; FUZZY SYSTEM; HUMAN; IMAGE ANALYSIS; IMAGE DISPLAY; INTERMETHOD COMPARISON; MELANOMA; PIGMENTATION; SKIN CANCER; STATISTICAL ANALYSIS;

ARTIFICIAL INTELLIGENCE; DATA INTERPRETATION, STATISTICAL; DERMOSCOPY; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MELANOMA; PATTERN RECOGNITION, AUTOMATED; SKIN NEOPLASMS;

EID: 48249158393     PISSN: 0909752X     EISSN: 16000846     Source Type: Journal    
DOI: 10.1111/j.1600-0846.2008.00301.x     Document Type: Article

References (23)

1. Jemal A, Siegel R, Ward E, Murray T, Xu J, Thun MJ. Cancer Statistics 2007. Cancer J Clin 2007; 57: 43-66.
2. Menzies SW, Crotty KA, Ingwar C, McCarthy WH. An atlas of surface microscopy of pigmented skin lesions: Dermoscopy. Sydney, Australia: McGraw-Hill, 2003.
3. Steiner K, Binder M, Schemper M, Wolff K, Pehamberger H. Statistical evaluation of epiluminescence dermoscopy criteria for melanocytic pigmented lesions. J Am Acad Dermatol 1993; 29: 581-588.
4. Binder M, Schwarz M, Winkler A, Steiner A, Kaider A, Wolff K, Pehamberger H. Epiluminescence microscopy. A useful tool for the diagnosis of pigmented skin lesions for formally trained dermatologists. Arch Dermatol 1995; 131: 286-291.
5. Fleming MG, Steger C, Zhang J, Gao J, Cognetta AB, Pollak I, Dyer CR. Techniques for a structural analysis of dermatoscopic imagery. Comput Med Imaging Graphics 1998; 22: 375-389.
6. Schmid P. Segmentation of digitized dermatoscopic images by two-dimensional color clustering. IEEE Trans Med Imaging 1999; 18: 164-171.
7. Gao J, Zhang J, Fleming MG, Pollak I, Cognetta AB. Segmentation of dermatoscopic images by stabilized inverse diffusion equations. Proc IEEE Int Conf. Image Process 1998; 3: 823-827.
8. Pagadala P. Tumor border detection in epiluminescence microscopy images. MS Thesis. Department of Electrical and Computer Engineering, University of Missouri, Rolla, 1998.
9. Donadey T, Serruys C, Giron A, Aitken G, Vignali J-P, Triller R, Fertil B. Boundary detection of black skin tumors using an adaptive radial-based approach. SPIE Med Imaging 2000; 3379: 810-816.
10. Cucchiara R, Grana C, Seidenari S, Pellacani G. Exploiting color and topological features for region segmentation with recursive fuzzy C-means. Machine Graphics Vision 2002; 11: 169-182.
11. Erkol B, Moss RH, Stanley RJ, Stoecker WV, Hvatum E. Automatic lesion boundary detection in dermoscopy images using gradient vector flow snakes. Skin Res Technol 2005; 11: 17-26.
12. 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.
13. Melli R, Grana C, Cucchiara R. Comparison of color clustering algorithms for segmentation of dermatological images. SPIE Med Imaging 2006; 6144: 3S1-3S9.
14. 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.
15. Nock R, Nielsen F. Statistical region merging. IEEE Trans Pattern Anal Machine Intelligence 2004; 26: 1452-1458.
16. Levkowitz H, Herman GT. GLHS: A generalized lightness, hue, and saturation color model. Graphic Models Image Process 1993; 55: 271-285.
17. Xu L, Jackowski M, Goshtasby A, Roseman D, Bines S, Yu C, Dhawan A, Huntley A. Segmentation of skin cancer images. Image Vision Comput 1999; 17: 65-74.
18. Pratt WK. Digital image processing: PIKS inside. Hoboken, NJ: John Wiley & Sons, 2007.
19. Felzenszwalb PF, Huttenlocher DP. Distance transforms of sampled functions. Cornell Computing and Information Science TR2004-1963, Available at: http://people.cs.uchicago.edu/~pff/papers/dt.pdf
20. Argenziano G, Soyer HP, De Giorgi V et al. Dermoscopy: A tutorial. Milan, Italy: EDRA Medical Publishing & New Media, 2002.
21. Hance GA, Umbaugh SE, Moss RH, Stoecker WV. Unsupervised color image segmentation with application to skin tumor borders. IEEE Eng Med Biol 1996; 15: 104-111.
22. Weiss B. Fast median and bilateral filtering. ACM Trans Graphics 2006; 25: 519-526.
23. Van Droogenbroeck M, Buckley M. Morphological erosions and openings: fast algorithms based on anchors. J Math Imaging Vision 2005; 22: 121-142.