Journal of Biomedical Engineering Research (대한의용생체공학회:의공학회지)

The Korean Society of Medical and Biological Engineering (대한의용생체공학회)
권호 표지
DOI QR코드

DOI QR Code

Pre-Processing for Determining Acral Lentiginous Melanoma(ALM)

말단흑색점흑색종 판별을 위한 전처리 과정

  • Ham, S.W. (Department of Electronics Engineering, College of Engineering, Ewha Womans University) ;
  • Oho, B.H. (Department of Skin Biology Laboratory and Skin Medicine, College of Medicine, Yonsei University) ;
  • Yang, S.J. (Ewha Institute of Convergence Medicine, Ewha Womans University Medical Center)
  • 함성원 (이화여자대학교 전자공학과) ;
  • 오병호 (연세대학교 의과대학 피부과학교실 및 피부생물학연구소) ;
  • 양세정 (이화여자대학교 의과대학 부속 목동병원)
  • Received : 2014.10.08
  • Accepted : 2014.12.16
  • Published : 2015.02.28
Download PDF
⟨ Previous Next ⟩

Abstract

Melanoma is originated from the melanocyte producing the melanin which determines the complexion, and it has the highest mortality among skin cancers. Acral lentiginous melanoma(ALM) arises from extremities such as hands, feet or fingernails. Since the appearance of ALM is different from melanoma on the body, conventional auto diagnosis systems for melanoma is inappropriate to detect ALM. Therefore, ALM is typically difficult to distinguish from general nevus, resulting in delayed diagnosis and bad prognosis. In this paper, we firstly introduce a determination method for ALM by dermatologists and propose a method to rotate dermoscopic images automatically as a pre-processing for facilitating the easy determination of ALM and to select the optimal value of the Gaussian differentiation filter parameter which is significant for precise pattern extraction using the scale space analysis. From experimental results, it is shown that there exists the consistency between empirical values of the Gaussian differential filter parameter and optimal values derived from the scale space analysis to distinguish nevus and ALM.

Keywords

References

  1. R. D. Cress and E. A. Holly, "Incidence of Cutaneous Melanoma among Non-Hispanic Whites, Hispanics, Asians, and Blacks: An Analysis of California Cancer Registry Data," 1988-93. Cancer Causes Control, vol. 8, pp. 246-252, 1997. https://doi.org/10.1023/A:1018432632528
  2. M. R. Roh, J. Y. Kim and K. Y. Chung, "Treatment and Outcomes of Melanoma in Acral Location in Korean Patients," Yonsei Med. J., vol. 51, pp. 562-568, 2010. https://doi.org/10.3349/ymj.2010.51.4.562
  3. G. Argenziano, H. P. Soyer, V. P. Giorgi, D. Piccolo, P. Carli and M. Delfino, "Dermoscopy: a Tutorial," 1st ed. Milan: Medical Publishing & New Media, pp. 10-11, 2000.
  4. R. P. Braun, H. S. Rabinovitz, M. Oliviero, A. W. Kopf, and J. H. Saurat, "Dermoscopy of Pigmented Skin Lesions," J. Am. Acad. Dermatol., vol. 52, pp. 109-121, 2005. https://doi.org/10.1016/j.jaad.2001.11.001
  5. I. Zalaudek, J. Kreusch, J. Giacomel, G. Ferrara, C. Catricala and G. Argenziano, "How to Diagnose Nonpigmented Skin Tumors: a Review of Vascular Structures Seen with Dermoscopy," part I. Melanocytic skin tumors J. Am. Acad. Dermatol., vol. 63, pp. 361-374, 2010.
  6. I. Zalaudek, "How to Diagnose Nonpigmented Skin Tumors: a Review of Vascular Structures Seen with Dermoscopy," part II. Nonmelanocytic skin tumors J. Am. Acad. Dermatol., vol. 63, pp. 377-386, 2010.
  7. J. Malvehy, S. Puig, G. Argenziano, A. A. Marghoob and H. P. Soyer, "Dermoscopy Report: Proposal for Standardization. Results of a Consensus Meeting of the International Dermoscopy Society," J. Am. Acad. Dermatol., vol. 57, pp. 84-95, 2007. https://doi.org/10.1016/j.jaad.2006.02.051
  8. H. Ganster, A. Pinz, R. Rohrer, E. Wildling, M. Binder and H. Kittler, "Automated Melanoma Recognition," IEEE Trans. on Medical Imaging, vol. 20, no. 3, pp. 233-239, 2001. https://doi.org/10.1109/42.918473
  9. P. Rubegni, G. Cevenini, M. Burroni, R. Perotti, G. Delleva, P. Sbano, C. Miracco, P. Luzi, P. Tosi, P. Barbini and L. Andreassi, "Automated Diagnosis of Pigmented Skin Lesions," Int'l J. Cancer, vol. 101, pp. 576-580, 2002. https://doi.org/10.1002/ijc.10620
  10. M. A. Sheha, M. S. Mabrouk and A. Sharawy, "Automatic Detection of Melanoma Skin Cancer using Texture Analysis," Int'l J. Computer Applications, vol. 42, no. 20, pp. 22-26, 2012.
  11. M. E. Celebi, H. Iyatomi and G. Schaefer, "Contrast Enhancement in Dermoscopy Images by Maximizing a Histogram Bimodality," Int'l Conf. on Image Processing, 2009, pp. 2601-2604.
  12. G. Schaefer, M. I. Rajab, M. E. Celebi and H. Iyatomi, "Skin Lesion Extraction in Dermoscopic Images Based on Colour Enhancement and Iterative Segmentation," IEEE Int. Conf. on Image Processing, 2009, pp. 3361-3364.
  13. A. A. Marghoob, J. Malvehy and R. P. Braun, An Atlas of Dermoscopy, Second Edition, London, UK: Informa, 2012.
  14. T. Saida, A. Miyazaki, S. Oguchi, Y. Ishihara, Y. Yamazaki, S. Murase, S. Yoshikawa, T. Tsuchida, Y. Kawabata and K. Tamaki, "Significance of dermoscopic patterns in detecting malignant melanoma on acral volar skin: results of a multicenter study in Japan," Arch. Dermatol., vol. 140, pp. 1233-1238, 2004.
  15. T. Lindeberg, "Feature Detection with Automatic Scale Selection," Int'l J. of Computer Vision, vol. 30, no. 2, pp. 79-116, 1998. https://doi.org/10.1023/A:1008045108935