Automatic Segmentation Methodology for Dermatological Images Acquired via Mobile Devices

Luís Rosado, Maria Vasconcelos

2015

Abstract

Nowadays, skin cancer is considered one of the most common malignancies in the Caucasian population, thus it is crucial to develop methodologies to prevent it. Because of that, Mobile Teledermatology (MT) is thriving, allowing patients to adopt an active role in their health status while facilitating doctors to early diagnose skin cancers. Skin lesion segmentation is one of the most important and difficult task in computerized image analysis process, and so far the attention is mainly turned to dermoscopic images. In order to turn MT more accurate, it is therefore fundamental to develop simple segmentation methodologies specifically designed for macroscopic images or images acquired via smartphones, which is the main focus of this work. The proposed method was applied in 80 images acquired via smartphones and promising results have been achieved: a mean Jaccard index of 81%, mean True Detection Rate of 96% and mean Accuracy around 98%. The major goal of this work is to develop a mobile application easily accessible for the general population, with the aim of raise awareness and help both patients and doctors in the early diagnosis of skin cancers.

References

  1. Cavalcanti, P., Scharcanski, J., Di Persia, L., and Milone, D. (2011). An ICA-based method for the segmentation of pigmented skin lesions in macroscopic images. In 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society,EMBC, pages 5993-5996.
  2. Cavalcanti, P., Yari, Y., and Scharcanski, J. (2010). Pigmented skin lesion segmentation on macroscopic images. In 25th International Conference of Image and Vision Computing New Zealand, pages 1-7.
  3. Fraunhofer, P. A. (2014). Melanoma detection, internal project. http://www.fraunhofer.pt/ en/fraunhofer aicos/projects/internal research/ melanoma detection.html.
  4. Fraz, M. M., Remagnino, P., Hoppe, A., Uyyanonvara, B., Rudnicka, A. R., Owen, C. G., and Barman, S. A. (2012). Blood vessel segmentation methodologies in retinal images-a survey. Computer methods and programs in biomedicine, 108(1):407-433.
  5. Ivanovici, M. and Stoica, D. (2012). Color diffusion model for active contours-an application to skin lesion segmentation. In 2012 Annual International Conference of the IEEE, Engineering in Medicine and Biology Society (EMBC), pages 5347-5350. IEEE.
  6. Jaccard, P. (1912). The distribution of the flora in the alpine zone. 1. New phytologist, 11(2):37-50.
  7. Mahmoud, M. and Al-Jumaily, A. (2011). Segmentation of skin cancer images based on gradient vector flow (GVF) snake. In 2011 International Conference on Mechatronics and Automation (ICMA), pages 216- 220.
  8. Manousaki, A. G., Manios, A. G., Tsompanaki, E. I., Panayiotides, J. G., Tsiftsis, D. D., Kostaki, A. K., and Tosca, A. D. (2006). A simple digital image processing system to aid in melanoma diagnosis in an everyday melanocytic skin lesion unit. a preliminary report. International Journal of Dermatology, 45(4):402-410.
  9. Otsu, N. (1979). A threshold selection method from graylevel histograms. IEEE Transactions on Systems, Man and Cybernetics, 9(1):62-66.
  10. Rosado, L., Castro, R., Ferreira, L., and Ferreira, M. (2012). Extraction of ABCD rule features from skin lesions images with smartphone. Studies in health technology and informatics, 177:242-247.
  11. Silveira, M., Nascimento, J. C., Marques, J. S., Marc¸al, A. R., Mendonc¸a, T., Yamauchi, S., Maeda, J., and Rozeira, J. (2009). Comparison of segmentation methods for melanoma diagnosis in dermoscopy images. IEEE Journal of Selected Topics in Signal Processing, 3(1):35-45.
  12. Tabatabaie, K., Esteki, A., and Toossi, P. (2009). Extraction of skin lesion texture features based on independent component analysis. Skin research and technology, 15(4):433-439.
Download


Paper Citation


in Harvard Style

Rosado L. and Vasconcelos M. (2015). Automatic Segmentation Methodology for Dermatological Images Acquired via Mobile Devices . In Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2015) ISBN 978-989-758-068-0, pages 246-251. DOI: 10.5220/0005178302460251


in Bibtex Style

@conference{healthinf15,
author={Luís Rosado and Maria Vasconcelos},
title={Automatic Segmentation Methodology for Dermatological Images Acquired via Mobile Devices},
booktitle={Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2015)},
year={2015},
pages={246-251},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005178302460251},
isbn={978-989-758-068-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2015)
TI - Automatic Segmentation Methodology for Dermatological Images Acquired via Mobile Devices
SN - 978-989-758-068-0
AU - Rosado L.
AU - Vasconcelos M.
PY - 2015
SP - 246
EP - 251
DO - 10.5220/0005178302460251