Computer aided Melanoma skin cancer detection using Image Processing

Procedia Computer Science

Volumn 48, Issue , 2015, Pages 736-741

  Jain, Shivangi ^a   Jagtap, Vandana ^a   Pise, Nitin ^a  

^a Dr Vishwanath Karad MIT World Peace University   (India)

Author keywords

Binary masks; Blob detection; Color threshloding; EdgeDetection; Image segmentation; Melona; Preprocessing; Skin cance

Indexed keywords

DERMATOLOGY; DIAGNOSIS; DISEASES; EDGE DETECTION; IMAGE ANALYSIS; IMAGE SEGMENTATION; INTELLIGENT COMPUTING; MEDICAL IMAGING; ONCOLOGY; TEXTURES;

BINARY MASKS; BLOB DETECTION; IMAGE PROCESSING TECHNIQUE; IMAGE PROCESSING TOOLS; MEDICAL IMAGE DIAGNOSIS; MELONA; PREPROCESSING; SQUAMOUS CELL CARCINOMA;

COLOR IMAGE PROCESSING;

EID: 84938930794     PISSN: None     EISSN: 18770509     Source Type: Conference Proceeding    
DOI: 10.1016/j.procs.2015.04.209     Document Type: Conference Paper

References (14)

1. A. Bono, S. Tomatis, and C. Bartoli, The ABCD system of melanoma detection: A spectrophotometric analysis of the asymmetry, border, color, and dimension, "Cancer", vol. 85, no. 1, pp. 72-77, January 1999
2. Pehamberger H, Binder M, Steiner A, Wolff K. In vivo epiluminescence microscopy: improvement of early diagnosis of melanoma. J Invest Dermatol, 100:356S-62S, 1993
3. Bafounta ML, Beauchet A, Aegerter P, Saiag P. Is dermoscopy (epiluminescence microscopy) useful for the diagnosis of melanoma? Results of a meta-analysis using techniques adapted to the evaluation of diagnostic tests. Arch Dermatol, 137:13, 43-50. 2001
4. G. Argenziano, H. Soyer, S. Chimenti, R. Talamini, R. Corona, F. Sera, and M. Binder, Dermoscopy of pigmented skin lesions: Results of consensus meeting via the Internet Journal of the American Academy of Dermatology, vol. 48, pp. 679-693, 2003
5. R. Garnavi, Computer-aided diagnosis of melanoma, Ph. D. dissertation, University of Melbourne, Australia, 2011
6. M. E. Celebi, H. Iyatomi, G. Schaefer, and W. V. Stoecker, Lesion border detection in dermoscopy images Computerised Medical Imaging and Graphics, vol. 33, no. 2, pp. 148-153, 2009
7. H. Iyatomi, H. Oka, M. Saito, A. Miyake, M. Kimoto, J. Yamagami, S. Kobayashi, A. Tanikawa, M. Hagiwara, K. Ogawa, G. Argenziano, H. P. Soyer, and M. Tanaka, Quantitative assessment of tumour extraction from dermoscopy images and evaluation of computer-based extraction methods for an automatic melanoma diagnostic system Melanoma Research, vol. 16, no. 2, pp. 183-190, 2006
8. R. Garnavi, M. Aldeen, M. E. Celebi, A. Bhuiyan, C. Dolianitis, and G. Varigos, Automatic segmentation of dermoscopy images using histogram thresholding on optimal color channels International Journal of Medicine and Medical Sciences, vol. 1, no. 2, pp. 126-134, 2010
9. V. Ng, B. Fung, and T. Lee, Determining the asymmetry of skin lesion with fuzzy borders Computers in Biology and Med., vol. 35, pp. 103-120, 2005
10. M. Celebi, H. Kingravi, B. Uddin, H. Iyatomi, Y. Aslandogan, W. Stoecker, and R. Moss, A methodological approach to the classification of dermoscopy images, Computerized Medical Imaging and Graphics, vol. 31, pp. 362-373, 2007
11. I. Maglogiannis and C. Doukas, Overview of advanced computer vision systems for skin lesions characterization IEEE Trans. on Information Technology in Biomedicine, vol. 13, no. 5, pp. 721-733, 2009
12. Otsu, N., A Threshold Selection Method from Gray-Level Histograms IEEE Transactions on Systems, Man, and Cybernetics, Vol. 9, No. 1, pp. 62-66, 1979
13. W. Stolz, A. Riemann, and A. Cognetta, ABCD rule of dermatoscopy: A new practical method for early recognition of malignant melanoma, European Journal of Dermatology, vol. 4, pp. 521-527, 1994
14. Z. She, Y. Liu, and A. Damatoa, Combination of features from skin pattern and ABCD analysis for lesion classification Skin Research and Technology, vol. 13, pp. 25-33, 2007