A prelimary approach for the automated recognition of malignant melanoma

Image Analysis and Stereology

Volumn 23, Issue 2, 2004, Pages 121-135

  Zagrouba, Ezzeddine ^a   Barhoumi, Walid ^b  

^a FACULTÉ DES SCIENCES DE TUNIS   (Tunisia)

^b UNIVERSITY OF MANOUBA   (Tunisia)

Author keywords

Fuzzy region growing; Karhunen Loeve transform; Melanoma recognition; Neural networks

Indexed keywords

EID: 33746509677     PISSN: 15803139     EISSN: 18545165     Source Type: Journal    
DOI: 10.5566/ias.v23.p121-135     Document Type: Article

References (42)

1. Astion M. L, Wilding P (1991). Application of neural networks to the interpretation of laboratory data in cancer diagnosis. Clin Chem 38(2):34-8.
2. Barhoumi W, Zagrouba E (2002). Boundaries Detection Based on Polygonal Approximation by Genetic Algorithms. Knowledge-Based Intelligent Information Engineering Systems and Allied Technologies (KES 2002), Frontiers in artificial intelligence and applications, IOS Press 82(2):621-7.
3. Barhoumi W, Zagrouba E, Solaiman B, Ghorbel E (2003). Fusion de l'information par la theorie de l'evidence: application en diagnostic du melanome. Proceedings of Conference Internationale Sciences Electroniques, Techniques de l'Information et des Telecommunications, SETIT, Tunisia.
4. Barron A (1993). Universal approximation bounds for superposition of a sigmoidal function. IEEE Trans Inform Theory 39:930-45.
5. Binder M, Steiner A, Schwarz M, Knollmayer S, Wolff K, Pehamberger H (1994). Application of an artificial neural network in epiluminescence microscopy pattern analysis of pigmented skin lesions: A pilot study. Brit J Dermatol 130(4):460-5.
6. Bostock RT, Claridge E, Harget AJ, Hall PN (1993). Towards a neural network based system for skin cancer diagnosis, Third International Conference on Artificial Neural Networks, 372:215-9.
7. Claridge E, Hall PN, Keefe M, Allen J (1992). Shape analysis for classification of malignant melanoma. J Biomed Eng 14:229-34.
8. Claridge E, Morris JD, Hall PN (1998). Evaluation of border irregularity in pigmented skin lesions against a consensus of expert clinicians. Proceedings of Medical Image Understanding and Analysis, UK, 85-8.
9. Cucchiara R, Grana C(2002). Using the topological tree for skin lesion structure description. Knowledge-Based Intelligent Information Engineering Systems and Allied Technologies (KES 2002), Frontiers in artificial intelligence and applications, IOS Press, 82(1):166-70.
10. Dreyfus G (2002). Les reseaux de neurones: pourqoui et pour quoi faire. Reseaux de neurones-Methodologies et Applications, Eds Eyrolles, 1-64.
11. Ercal F, Chawla A, Lee HC (1994). Neural network diagnosis of malignant melanoma from color images. IEEE Trans Biomed Eng 41(9):837-45.
12. Fitzpatrick TB, Rhodes AJ, Sober A, Mihm M (1988). Primary malignant melanoma of the skin: the call for action to identify persons at risk, to discover precursor lesions, to detect early melanomas. Pigm Cell Res 9(1):110-7.
13. Ganster H, Pinz A, Rohrer E, Wildling E, Binder M, Kittler H (2001). Automated melanoma recognition. IEEE Trans Med Imaging 20(3):233-9.
14. Guthowicz-Krusin D, Elbaum M, Szwaykowski P, Kopf AW (1997). Can early malignant melanoma be differentiated from atypical melanocytic nevus by in vivo techniques? Part II. Automatic machine vision classification. Skin Res Technol 3:15-22.
15. Haykin S (1999). Neural networks: A comprehensive fundation. Upper Saddle Rever, New Jersey: Prentice Hall.
16. Healsmith MF, Bourke JE, Graham-Brown RC (1994). An evaluation of the revised seven-point checklist for the early diagnosis of cutaneous malignant melanoma. Brit J Dermat 130:48-50.
17. Hintz-Madsen M, Hansen LK, Larsen J, Drzewiecki KT (1996). Detection of malignant melanoma using neural classifiers. in solving engineering problems with neural networks, International Conference on Engineering Applications using Neural Networks EANN, Turku, Finland, 395-8.
18. Huberty CJ (1994). Applied discriminant analysis. New York: John Wiley and Sons, Inc.
19. Kaufman L, Rousseeuw P. J (1990). Finding groups in data. New York: John Wiley and Sons, Inc.
20. Khanfir I, Kallel A, Taouil K, Bouhlel MS, Kammoun L (2002). Segmentation d'images par seuillage d'histogrammes: Application a l'analyses de melanomes. JTEA'2002, Tunisia, 190-8.
21. Keefe M, Dick D, Wakkel R (1990). A study of the value of the seven-point checklist in distinguishing benign pigmented lesions from melanoma. Clin Exp Dermatol 15:167-71.
22. Lee KT, Gallagher R, Coldman A, McLean D (1997). Dullrazor: A software approach to hair removal from images. Comput Biol Med 27:533-43.
23. Lee KT (2001). Measuring border irregularity and shape of cutaneous melanocytic lesions. PhD, Simon Fraser University, Canada.
24. Loeve M (1998) Fonctions aléatoires de second ordre. Levy P, editor, Processus Stochastiques et Mouvement Brownien, Hermann.
25. Lorentzen H, Wiesmann K, Kenet RO, Secher L, Larsen FG (2000). Comparaison of dermatoscopic ABCD rule and risk stratification in the diagnosis of malignant melanoma. Acta Derm-Venereol 80(2):122-6.
26. Mandelbrot BB (1982). The fractal geometry of nature. New York: W.H. Freeman and Co.
27. Ng V, Lee T (1996). Measuring border irregularities of skin lesions using fractal dimensions. In SPIE Photonics China, Electronic Imaging and Multimedia Systems, Beijing, 64-72.
28. Pehamberger H, Steiner A, Wolff K (1987). In vivo epiluminescence microscopy of pigmented skin lesions. Pattern analysis of pigmented skin lesions. J Am Acad Dermtol 17(4):571-83.
29. Pentland A (1984). Fractals: A model for both texture and shading. In Hoffman (Eds) Annual Meeting of the Optical Society of America: MIT.
30. Rangayyan RM, El-Faramawy NM, Desautels J, Alim OA (1997). Measures of acutance and shape for classification of breast tumors. IEEE Trans Med Imaging, 16(6):199-210.
31. Ripley BB (1996). Pattern recognition and neural networks. Cambridge University Press.
32. Rumelhart DE, Hinton GE, Williams RJ (1988). Learning internal representations by error propagation. In parallel distributed processing-explorations in the microstructure of cognition, 1:318-62.
33. Sboner A, Blanzieri E, Eccher C, Bauer P, Cristofolini M, Zumiani G, Forti S (2001). A knowledge based system for early melanoma diagnosis support. Proceedings of the 6th IDAMAP workshop-Intelligent Data Analysis in Medicine and Pharmacology, London, UK.
34. Schmid-Saugeon P, Guillod J, Thiran JP (2003). Towards a computer-aided diagnosis system for pigmented skin lesions. Computerized Medical Imaging and Graphics., 27:65-78.
35. Sonka M, Hlavac V, Boyle R (1993). Image processing, analysis and machine vision. London: Chapman & Hall.
36. Stolz W, Braun-Falco O, Bilek P, Landthaler M, Cognetta AB (1994). Color atlas of dermatoscopy. Berlin: Blackwell Science Ltd.
37. Tamura H, Mori S, Yamawaki T (1978). Textual features corresponding to visual perception. IEEE Trans Syst Man and Cyb 8:460-73.
38. Taouil K, Bouhlel MS, Elloumi, Kammoun L (2002). Quantification des caracteristiques de melanomes en vue d'une classification. JTEA'2002, Sousse Nord, Tunisia, 179-85.
39. Voight H, ClaBen R (1995). Topodermatographic image analysis for melanoma screening and the quantative assessment of tumor dimension parameters of the skin. Cancer 75(4):981-8.
40. Zagrouba E, Barhoumi W (2002). Semiautomatic detection oftumoral zone. Image Anal Stereol 21(1):13-8.
41. Zagrouba E, Barhoumi W (2003a). Objective and cost-efficient approach for skin lesions classification. Proceedings of the IEEE/ACS International Conference on Computer Systems and Applications, Gammarth, Tunisia, 135-6.
42. Zagrouba E, Barhoumi W (2003b). Systme d'aide au diagnostic du melanome pour les dermatologues. 1ereConference Francophone en Gestion et Ingenierie des Systemes Hospitaliers, GISEH'2003, Lyon, France, 616-25.