BibTex RIS Kaynak Göster

Segmentation Strategies in Dermoscopy to Follow-up Melanoma: Combined Segmentation Scheme

Yıl 2015, Cilt: 5 Sayı: 3, 56 - 61, 23.07.2016

Öz

— Image processing techniques constitutes an important tool to improve skin cancer diagnose, whose early detection is still the most relevant prognostic factor. Nowadays, the follow-up of suspicious melanocytic skin lesions using standard protocols is possible after the development of digital image technology, enhancing the early detection strategy of the skin cancer diagnose. The correct selection of the borders in these particular images of skin microscopy is sometimes demanding, as these images possess particular artifacts (hairs and air bubbles).A stable algorithm to segment the border of the lesion is also important when the following up of suspicious melanocytic lesions uses quantitative markers, as accessing the geometry of the growth border, symmetry, area, among others. In this paper a new strategy to segment dermoscopy images is presented by merging two different approaches in image processing, the Empirical Mode Decomposition of the HilbertHuang Transform to remove common artifacts, followed by a Local Normalization to improve segmentation

Kaynakça

  • Baumert, J., Schmidt, M., Giehl, K.A. (2009), Time trends in tumor thickness vary in sub- groups: analysis of 6475 patients by age, tumour site and melanoma subtype. Melanoma Res 2009; 19:24-30.
  • Boyle, P., Doré. J.F., Autier, P., Ringborg, U. (2004). Cancer of the skin: a forgotten problem in Europe. Annals of Oncology 15:5-6.
  • Celebi, M. E., Iyatomi, H., Schaefer, G., Stoecker, W. (2007). Lesion border detection in dermoscopy images. Computerized Medical Imaging and Graphics, vol 33, Issue 2, pp 148-153.
  • Celebi, M. E., Aslandogan,Y. A., Stoecker,W.V., Iyatomi, H., Oka, H., Chen, X. (2006). Unsupervised Border Detection in Dermoscopy Images, SkinResearch and Technology.
  • Erkol, B., Moss, R.H., Stanley, R.J., Stoecker, W.V., Hvatum, E.(2005). Automatic Lesion Boundary Detection in Dermoscopy Images Using Gradient Vector Flow Snakes, Skin Research and Technology, 11(1): 17-26.
  • Ferlay, J., Steliarova-Foucher, E., Lortet-Tieulent, J., Rosso, S., Coebergh, J.W.W., Comber, H., Forman, D., Bray, F.(2012). Cancer incidence and mortality patterns in Europe: estimates for 40 countries in 2012. Eur J Cancer.
  • Fonseca-Pinto, R., Ducla-Soares, J. L., Araújo, F., Aguiar, P., Andrade, A. (2009). On the influence of timeseries length in EMD to extract frequency content: Simulations and models in biomedical signals, Medical Engineering & Physics 31 713–719.
  • Fonseca-Pinto, R., Caseiro, P., Andrade, A. (2010). Bi-dimensional Empirical Mode Decomposition (BEMD) in dermoscopic images: artefact removal and border lesion detection. Proceedings of the 7th IASTED international Conference Signal Processing, Pattern Recognition and Applications; 341-345.
  • Haylo, N., Rahman. Z., Park, S. (2001). Information content in nonlinear local normalization processing of digital images, Proc. SPIE 4388, Visual Information Processing X, 129.
  • Holterhues, C., Vries, E., Louwman, M.W. (2010). Incidence and trends of cutaneous malignancies in the Netherlands, 1989-2005, J Invest Dermatol 2010; 130:1807-12.
  • Huang, N.E., Shen, Z., Long, S.R., Wu, M.C., Shih, H.H., Zheng, Q., Yen, N-C., Tung, C.C., Liu, HH., (1998).
  • The Empirical mode decomposition and the Hilbert spectrum for nonlinear and nonstationary time-series analysis. Proc R Soc Lond A 454: 903- 995.
  • Iyatomi, H., Oka, H., Saito, M. (2006). Quantitative Assessment of Tumor Extraction from Dermoscopy Images and Evaluation of Computer-based Extraction Methods for Automatic Melanoma Diagnostic System. Melanoma Research, 16(2): 183-190, 2006.
  • Kasper, Dennis L; Braunwald, Eugene; Fauci, Anthony; et al. Harrison’s Principles of Internal Medicine, 16th ed. New York: McGraw-Hill, 2005.
  • Melli, R., Grana, C., Cucchiara, R.(2006). Comparison of Color Clustering Algorithms for Segmentation of Dermatological Image, Proc. of the SPIE Medical Imaging Conf., 6144: 3S1-9.
  • Pereira, J. M., A. Nogueira, C. Baptista, D. Fonseca-Pinto, R. (2015). An adaptive approach for skin lesion segmentation in dermoscopy images using a multiscale Local Normalization. Proceedings of the CIM series in Mathematical Sciences, Springer-Verlang (accepted)
  • Sant, M., Allemani, C., Santaquilani, M. (2009). EUROCARE-4.Survival of cancer patients diagnosed in 1995- 1999. Results and commentary, Eur J Cancer, 45:931-91.
Yıl 2015, Cilt: 5 Sayı: 3, 56 - 61, 23.07.2016

Öz

Kaynakça

  • Baumert, J., Schmidt, M., Giehl, K.A. (2009), Time trends in tumor thickness vary in sub- groups: analysis of 6475 patients by age, tumour site and melanoma subtype. Melanoma Res 2009; 19:24-30.
  • Boyle, P., Doré. J.F., Autier, P., Ringborg, U. (2004). Cancer of the skin: a forgotten problem in Europe. Annals of Oncology 15:5-6.
  • Celebi, M. E., Iyatomi, H., Schaefer, G., Stoecker, W. (2007). Lesion border detection in dermoscopy images. Computerized Medical Imaging and Graphics, vol 33, Issue 2, pp 148-153.
  • Celebi, M. E., Aslandogan,Y. A., Stoecker,W.V., Iyatomi, H., Oka, H., Chen, X. (2006). Unsupervised Border Detection in Dermoscopy Images, SkinResearch and Technology.
  • Erkol, B., Moss, R.H., Stanley, R.J., Stoecker, W.V., Hvatum, E.(2005). Automatic Lesion Boundary Detection in Dermoscopy Images Using Gradient Vector Flow Snakes, Skin Research and Technology, 11(1): 17-26.
  • Ferlay, J., Steliarova-Foucher, E., Lortet-Tieulent, J., Rosso, S., Coebergh, J.W.W., Comber, H., Forman, D., Bray, F.(2012). Cancer incidence and mortality patterns in Europe: estimates for 40 countries in 2012. Eur J Cancer.
  • Fonseca-Pinto, R., Ducla-Soares, J. L., Araújo, F., Aguiar, P., Andrade, A. (2009). On the influence of timeseries length in EMD to extract frequency content: Simulations and models in biomedical signals, Medical Engineering & Physics 31 713–719.
  • Fonseca-Pinto, R., Caseiro, P., Andrade, A. (2010). Bi-dimensional Empirical Mode Decomposition (BEMD) in dermoscopic images: artefact removal and border lesion detection. Proceedings of the 7th IASTED international Conference Signal Processing, Pattern Recognition and Applications; 341-345.
  • Haylo, N., Rahman. Z., Park, S. (2001). Information content in nonlinear local normalization processing of digital images, Proc. SPIE 4388, Visual Information Processing X, 129.
  • Holterhues, C., Vries, E., Louwman, M.W. (2010). Incidence and trends of cutaneous malignancies in the Netherlands, 1989-2005, J Invest Dermatol 2010; 130:1807-12.
  • Huang, N.E., Shen, Z., Long, S.R., Wu, M.C., Shih, H.H., Zheng, Q., Yen, N-C., Tung, C.C., Liu, HH., (1998).
  • The Empirical mode decomposition and the Hilbert spectrum for nonlinear and nonstationary time-series analysis. Proc R Soc Lond A 454: 903- 995.
  • Iyatomi, H., Oka, H., Saito, M. (2006). Quantitative Assessment of Tumor Extraction from Dermoscopy Images and Evaluation of Computer-based Extraction Methods for Automatic Melanoma Diagnostic System. Melanoma Research, 16(2): 183-190, 2006.
  • Kasper, Dennis L; Braunwald, Eugene; Fauci, Anthony; et al. Harrison’s Principles of Internal Medicine, 16th ed. New York: McGraw-Hill, 2005.
  • Melli, R., Grana, C., Cucchiara, R.(2006). Comparison of Color Clustering Algorithms for Segmentation of Dermatological Image, Proc. of the SPIE Medical Imaging Conf., 6144: 3S1-9.
  • Pereira, J. M., A. Nogueira, C. Baptista, D. Fonseca-Pinto, R. (2015). An adaptive approach for skin lesion segmentation in dermoscopy images using a multiscale Local Normalization. Proceedings of the CIM series in Mathematical Sciences, Springer-Verlang (accepted)
  • Sant, M., Allemani, C., Santaquilani, M. (2009). EUROCARE-4.Survival of cancer patients diagnosed in 1995- 1999. Results and commentary, Eur J Cancer, 45:931-91.
Toplam 17 adet kaynakça vardır.

Ayrıntılar

Diğer ID JA56EA47PR
Bölüm Makaleler
Yazarlar

Jorge Pereira Bu kişi benim

Rui Fonseca-pinto Bu kişi benim

Yayımlanma Tarihi 23 Temmuz 2016
Yayımlandığı Sayı Yıl 2015 Cilt: 5 Sayı: 3

Kaynak Göster

APA Pereira, J., & Fonseca-pinto, R. (2016). Segmentation Strategies in Dermoscopy to Follow-up Melanoma: Combined Segmentation Scheme. TOJSAT, 5(3), 56-61.
AMA Pereira J, Fonseca-pinto R. Segmentation Strategies in Dermoscopy to Follow-up Melanoma: Combined Segmentation Scheme. TOJSAT. Temmuz 2016;5(3):56-61.
Chicago Pereira, Jorge, ve Rui Fonseca-pinto. “Segmentation Strategies in Dermoscopy to Follow-up Melanoma: Combined Segmentation Scheme”. TOJSAT 5, sy. 3 (Temmuz 2016): 56-61.
EndNote Pereira J, Fonseca-pinto R (01 Temmuz 2016) Segmentation Strategies in Dermoscopy to Follow-up Melanoma: Combined Segmentation Scheme. TOJSAT 5 3 56–61.
IEEE J. Pereira ve R. Fonseca-pinto, “Segmentation Strategies in Dermoscopy to Follow-up Melanoma: Combined Segmentation Scheme”, TOJSAT, c. 5, sy. 3, ss. 56–61, 2016.
ISNAD Pereira, Jorge - Fonseca-pinto, Rui. “Segmentation Strategies in Dermoscopy to Follow-up Melanoma: Combined Segmentation Scheme”. TOJSAT 5/3 (Temmuz 2016), 56-61.
JAMA Pereira J, Fonseca-pinto R. Segmentation Strategies in Dermoscopy to Follow-up Melanoma: Combined Segmentation Scheme. TOJSAT. 2016;5:56–61.
MLA Pereira, Jorge ve Rui Fonseca-pinto. “Segmentation Strategies in Dermoscopy to Follow-up Melanoma: Combined Segmentation Scheme”. TOJSAT, c. 5, sy. 3, 2016, ss. 56-61.
Vancouver Pereira J, Fonseca-pinto R. Segmentation Strategies in Dermoscopy to Follow-up Melanoma: Combined Segmentation Scheme. TOJSAT. 2016;5(3):56-61.