346
Views
0
CrossRef citations to date
0
Altmetric
Articles

Construction of adaptive pulse coupled neural network for abnormality detection in medical images

&

References

  • Ballerini, L., et al. 2012. Non-melanoma skin lesion classification using colour image data in a hierarchical K-NN classifier. 9th IEEE International Symposium on Biomedical Imaging (ISBI) :358–361.
  • Ballerini, L., et al. 2013. A color and texture based hierarchical K-NN approach to the classification of non-melanoma skin lesions. In: Celebi M., Schaefer G. (eds.), Color Medical Image Analysis. Lecture Notes in Computational Vision and Biomechanics, vol 6. Springer.
  • Blasdel, G. G., and D. Campbell. 2001. Functional retinotopy of monkey visual cortex. The Journal of Neuroscience 21 (20):8286–8301. doi:10.1523/JNEUROSCI.21-20-08286.2001.
  • Cai, W., et al. 2008. Adaptive image segmentation using modified pulse coupled neural network. Advancement Neural Networks - Isnn 2008, Pt 2, Proceedings 5264:794–800.
  • Hassanien, A. E., and J. M. Ali. 2004. Digital mammogram segmentation algorithm using pulse coupled neural networks. Image Graph 2004 Proceedings Third International Conference (December):92–95.
  • Iyatomi, H., et al. 2008. An improved Internet-based melanoma screening system with dermatologist-like tumor area extraction algorithm. Computerized Medical Imaging and Graphics. 32 (7):566–579. doi:10.1016/j.compmedimag.2008.06.005.
  • Johnson, J. L., and M. L. Padgett. 1999. PCNN models and applications. IEEE Transactions Neural Networks 10 (3):480–498. doi:10.1109/72.761706.
  • Kapur, J. N., P. K. Sahoo, and A. K. C. Wong. 1985. A new method for gray-level picture thresholding using the entropy of the histogram. Journal of Computer Vision, Graphics, and Image Processing. 29 (3):273–285. Elsevier.
  • Laskaris, N., et al. 2010. Fuzzy description of skin lesions. Perception 44 (1):762717–762717–10.
  • Li, J., et al. 2013. Image segmentation with PCNN model and immune algorithm. Journal Computation. 8 (9):2429–2436. doi:10.4304/jcp.8.9.2429-2436.
  • Monica Subashini, M., and S. K. Sahoo. 2014. Pulse coupled neural networks and its applications. Expert Systems Applications 41 (8):3965–3974. doi:10.1016/j.eswa.2013.12.027.
  • Mishra, N. K., and M. Emre Celebi. 2016. An Overview of Melanoma Detection in Dermoscopy Images Using Image Processing and Machine Learning. CoRR, Vol: abs/1601.07843.
  • Pal, N. R., and S. K. Pal. 1991. Image model, poisson distribution and object extraction. International Journal of Pattern Recognition and Artificial Intelligence 5 (3):459–483. doi:10.1142/S0218001491000260.
  • Qi, C. 2014. Maximum entropy for image segmentation based on an adaptive particle swarm optimization. Applications Mathematical Information Sciences 8 (6):3129–3135. doi:10.12785/amis/080654.
  • Rajalakshmi, T., and S. Prince. 2016. Retinal model-based visual perception: Applied for medical image processing. Biologically Inspired Cognitive Architectures. 18:95–104. doi:10.1016/j.bica.2016.09.005.
  • Ranganath, H. S., G. Kuntimad, and J. L. Johnson. 1995. Pulse coupled neural networks for image processing. Southeastcon’95 Vision Futur Proceedings., March 26-29. IEEE 37–43.
  • Rigel, D. S., J. Russak, and R. Friedman. 2010. The evolution of melanoma diagnosis: 25 years beyond the ABCDs. CA Cancer Journal Clinical 60 (5):301–316. doi:10.3322/caac.20074.
  • Srivastava, N., et al. 2014. Dropout: A simple way to prevent neural networks from overfitting. Journal Machine Learning Researcher 15:1929–1958.
  • Tovee, M. J. 1994. How fast is the speed of thought?. Neuronal processing 4 (12):1125–1127.
  • Wang, H., et al. 2010. A simplified pulse-coupled neural network for cucumber image segmentation. Proceedings - 2010 International Conference Computation Information Sciences ICCIS Dec 7-19. IEEE, 1053–1057.
  • Wise, R. 2007. Optoelectronic implementations of pulse coupled neural network: Challenges and limitations. Cambridge, MA: Massachusetts Institute of Technology (MIT).
  • Zanottoa, M., and L. Ballerinib. 2011. Visual cues do not improve lesion ABC (D) grading. Proceedings of SPIE Medical Imaging, Orlando. Vol 796600:1–10
  • Zhang, Y., and L. Wu. 2008. Pattern recognition via PCNN and Tsallis entropy. Sensors 8 (11):7518–29. doi:10.3390/s8117518.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.