Quantitative assessment of tumour extraction from dermoscopy images and evaluation of computer-based extraction methods for an automatic melanoma diagnostic system

Melanoma Research

Volumn 16, Issue 2, 2006, Pages 183-190

  Iyatomi, Hitoshi ^a   Oka, Hiroshi ^a   Saito, Masataka ^a   Miyake, Ayako ^a   Kimoto, Masayuki ^a   Yamagami, Jun ^a   Kobayashi, Seiichiro ^a   Tanikawa, Akiko ^a   Hagiwara, Masafumi ^a   Ogawa, Koichi ^a   Argenziano, Giuseppe ^a   Peter Soyer, H ^a   Tanaka, Masaru ^a  

^a KEIO UNIVERSITY   (Japan)

Author keywords

Automatic tumour extraction; Clark naevus; Dermoscopy; Melanoma; Reed naevus; Remote diagnosis

Indexed keywords

ACCURACY; ALGORITHM; AREA UNDER THE CURVE; ARTICLE; AUTOMATION; CANCER DIAGNOSIS; CANCER SURGERY; COMPUTER ASSISTED SURGERY; CONTROLLED STUDY; DIAGNOSTIC ACCURACY; EPILUMINESCENCE MICROSCOPY; HUMAN; IMAGE ANALYSIS; MELANOMA; NEVUS; OBSERVER VARIATION; PERFORMANCE; PRIORITY JOURNAL; QUANTITATIVE ANALYSIS; RECEIVER OPERATING CHARACTERISTIC; STATISTICAL ANALYSIS; COMPARATIVE STUDY; IMAGE PROCESSING; METHODOLOGY; REPRODUCIBILITY; ROC CURVE; SENSITIVITY AND SPECIFICITY; SKIN TUMOR;

ALGORITHMS; AREA UNDER CURVE; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MELANOMA; REPRODUCIBILITY OF RESULTS; ROC CURVE; SENSITIVITY AND SPECIFICITY; SKIN NEOPLASMS;

EID: 33745894963     PISSN: 09608931     EISSN: None     Source Type: Journal    
DOI: 10.1097/01.cmr.0000215041.76553.58     Document Type: Article

References (23)

1. Stolz W, Braun-Falco O, Bliek P, Kandthaler M, Burgdorf WHC, Congnetta AB. Color atlas of dermatoscopy. 2nd ed. Berlin: Blackwell Publishing; 2002.
2. Meyskens FLJr, Berdeaux DH, Parks B, Tong T, Loescher L, Moon TE. Cutaneous malignant melanoma (Arizona Cancer Center experience). I. Natural history and prognostic factors influencing survival in patients with stage I disease. Cancer 1988; 62:1207-1214.
3. Buttner P, Garbe C, Bertz J, Birg G, d'Hoedt B, Drepper H, et al. Primary cutaneous melanoma. Optimized cutoff points of tumor thickness and importance of Clark's level for prognostic classification. Cancer 1995; 75:2499-2506.
4. Garbe C, Buttner P, Bertz J, Berg G, d'Hoedt B, Drepper H, et al. Primary cutaneous melanoma. Identification of prognostic groups and estimation of individual prognosis for 5093 patients. Cancer 1995; 75:2484-2491.
5. Balch CM, Murad TM, Soong SJ, Ingalls AL, Richards PC, Maddox WA. Tumor thickness as a guide to surgical management of clinical stage I melanoma patients. Cancer 1979; 43:883-888.
6. Soyer HP, Smolle J, Kerl H, Stettner H. Early diagnosis of malignant melanoma by surface microscopy. Lancet 1987; 2:803.
7. Ercal F, Chawla A, Stoecker WV, Lee HC, Moss RH. Neural network diagnosis of malignant melanoma from color images. IEEE Trans Biomed Eng 1994; 9:837-845.
8. Seidenari S, Pellacani G, Pepe P. Digital videomicroscopy improves diagnostic accuracy for melanoma. J Am Acad Dermatol 1998; 39:175-181.
9. Elbaum M, Kopf AW, Rabinovitz HS, Langley RG, Mihm MC Jr. Automatic differentiation of melanoma from melanocytic nevi with multispectral digital dermoscopy: a feasibility study. J Am Acad Dermatol 2001; 44:207-218.
10. Rubegni P, Burroni M, Cevenini G, Peorotti R, Dell'Eva G, Barbini P, 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.
11. Rubegni P, Cevenini G, Burroni M, Peotti R, Dell'Eva G, Sbano P, et al. Automated diagnosis of pigmented skin lesions. Int J Cancer 2002; 101:576-580.
12. Blum A, Soyer HP, Garbe C, Kerl H, Rassner G, Hofman-Wellenhof R. The dermoscopic classification of atypical melanocytic naevi (Clark naevi) is useful to discriminate benign from malignant melanocytic lesions. Br J Dermatol 2003; 149:1159-1164.
13. Oka H, Hashimoto M, Iyatomi H, Argenziano G, Soyer HP, Tanaka M. Internet-based program for automatic discrimination of dermoscopic images between melanomas and Clark naevi. Br J Dermatol 2004; 150:1041.
14. Green A, Martin N, Pfitzner J, O'Rourke M, Knight N. Computer image analysis in the diagnosis of melanoma. J Am Acad Dermatol 1994; 31:958-964.
15. Jackowski LXM, Goshtasby A, Yu C, Roseman D, Bines S, Dhawan A, et al. Segmentation of skin cancer images, Image vis. Computing 1999; 17: 65-74.
16. 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.
17. Schmid P. Segmentation of digitalized dermatoscopic images by two-dimensional color clustering. IEEE Trans Med Imag 1999; 18:164-172.
18. Ganster H, Pinz A, Roehrer R, Wilding E, Binder M, Kitter H. Automated melanoma recognition. IEEE Trans Med Imag 2001; 20:233-239.
19. Schmid-Saugeon P, Guillod J, Thiran JP. Towards a computer-aided diagnosis system for pigmented skin lesions. Comput Med Imaging Graph 2003; 27:65-78.
20. Iyatomi H, Oka H, Miyake A, Kimoto M, Tanaka M, Ogawa K. A practical internet-based melanoma diagnostic system. In: IEEE, editor. Proceedings of IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), November 14-15, San Diego, CA, USA, 2005. Piscataway, NJ: IEEE Press; 2005. pp. 443-446.
21. Guillod J, Schmid-Saugeon P, Guggisberg D, Cerottini JP, Braun R, Krischer JH, et al. Validation of segmentation techniques for digital dermoscopy. Skin Res Technol 2002; 8:240-249.
22. Rosenfeld L, Morville P. Information architecture for the world wide web. 2nd ed. San Francisco: O'Reilly; 2002.
23. Otsu N. An automatic threshold selection method based on discriminant and least square criteria. Trans IEICE 1988; 63:349-356.