Advertisement

An Acceleration of Improved Segmentation Methods for Dermoscopy Images Using GPU

  • Pawan Kumar Updhyay
  • Satish Chandra
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 524)

Abstract

Medical images have made a great influence on medicine, diagnosis, and treatment. The essential element of medical image processing is segmentation for identifying the region of interest. The fundamental methods of image segmentation are unable to process the large dataset of images and require scaling to make them more interactive. In order to address this issue, an exponential entropy-based image segmentation methods are proposed which are based on boundary demarcation, contour- and learning-based approaches. To accelerate these methods on graphical processing unit, a well-defined concept of memory preallocation and vectorization are incorporated in the novel approach. Results have been investigated on 240 gold standard dermoscopy images. These results reveal that the optimized methods of segmentation are computationally benefited from GPU processing in terms of speed and accuracy for skin lesion detection.

Keywords

Adaptive thresholding Active snake Pulse-coupled neural network Graphical processing unit Exponential entropy Dermoscopy images 

References

  1. 1.
    Abramov, A., & Kulvicius, T. (2010). Real-time image segmentation on a GPU. In Facing the Multicore-Challenge (pp. 131–142).Google Scholar
  2. 2.
    Boyer, V., & El Baz, D. (2013). Recent advances on GPU computing in operations research. In 2013 IEEE International Symposium on Parallel and Distributed Processing Workshops and PhD Forum (pp.1778–1787).Google Scholar
  3. 3.
    Engineering, C. (2011). Medical image denoising using aaptive. 2(2), 52–58.Google Scholar
  4. 4.
    Khattak, S. S., Saman, G., Khan, I., & Salam, A. (2015). Maximum entropy based image segmentation of human skin lesion. 9(5), 1060–1064.Google Scholar
  5. 5.
    Silveira, M., Nascimento, J. C., Marques, J. S., Marçal, A. R. S., Mendonça, T., Yamauchi, S., et al. (2009). Comparison of segmentation methods for melanoma diagnosis in dermoscopy images. 3(1), 35–45.Google Scholar
  6. 6.
    Kim, C. H., & Lee, Y. J. (2015). Medical image segmentation by improved 3D adaptive thresholding (pp. 263–265).Google Scholar
  7. 7.
    Fulkerson, B., & Soatto, S. (2012). Really quick shift: Image segmentation on a GPU. Lecture Notes in Computer Science (including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6554, pp. 350–358) LNCS.CrossRefGoogle Scholar
  8. 8.
    Smistad, E., Falch, T. L., Bozorgi, M., Elster, A. C., & Lindseth, F. (2015). Medical image segmentation on GPUs—a comprehensive review.Google Scholar
  9. 9.
    Güler, Z., & Cinar, A. (2013). GPU-based image segmentation using level set method with scaling approach. Computer Science & Information Technology, 81–92.Google Scholar
  10. 10.
    Ghorpade, J., Parande, J., Kulkarni, M., & Bawaskar, A. (2012). Gpgpu processing in cuda architecture. Advanced Computing: An International Journal, 3(1), 105–120.Google Scholar
  11. 11.
    Singh, T. R., Roy, S., Singh, O. I., Sinam, T., & Singh, K. M. (2011). A new local adaptive thresholding technique in binarization. 8(6), 271–277.Google Scholar
  12. 12.
    Srivastava, N., Hinton, G. E., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: A simple way to prevent neural networks from overfitting. The Journal of Machine Learning Research, 15, 1929–1958.MathSciNetzbMATHGoogle Scholar
  13. 13.
    Johnson, J. L., & Padgett, M. L. (1999). PCNN models and applications. IEEE Transactions on Neural Networks, 10(3), 480–498.CrossRefGoogle Scholar
  14. 14.
    Wang, H., Ji, C., Gu, B., & Tian, G. (2010). A simplified pulse-coupled neural network for cucumber image segmentation. In Proceedings of 2010 International Conference on Computational and Information Sciences ICCIS (pp. 1053–1057).Google Scholar
  15. 15.
    Li, J., Zou, B., Ding, L., & Gao, X. (2013). Image segmentation with PCNN model and immune algorithm. Journal of Computers, 8(9), 2429–2436.Google Scholar
  16. 16.
    Emre Celebi, M., & Mishra, N. K. (2016). An overview of melanoma detection in dermoscopy images using processing and machine learning. Arxiv Statistics—Machine Learning, 1–15.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of CSE & ITJIIT UniversityNoidaIndia

Personalised recommendations