LM, RP, and GD Based ANN architecture models for Biomedical Image Compression

G. Vimala Kumari *, G. Sasibhushana Rao**, B. Prabhakara Rao***
* Assistant Professor, Department of Electronics and Communication Engineering, MVGR College of Engineering, Vizianagaram,Andhra Pradesh, India.
**Professor, Department of Electronics & Communication Engineering, Andhra University College of Engineering, Visakhapatnam,Andhra Pradesh, India.
*** Programme Director, School of Nanotechnology, JNTU, Kakinada, Andhra Pradesh, India.
Periodicity:July - September'2018
DOI : https://doi.org/10.26634/jip.5.3.15195

Abstract

The aim of this paper is to present an image compression method using feedforward backpropagation neural networks. Medical imaging is an efficient source for better diagnosis of the disease and also helps in assessing the severity of the disease. But due to the increasing size of the medical images, transferring and storage of images require huge bandwidth and storage space. Therefore, it is essential to derive an effective compression algorithm, which have minimal loss, time complexity, and increased reduction in size. With the concept of Neural Network, data compression can be achieved by producing an internal data representation. The training algorithm and development architecture gives less distortion and considerable compression ratio and also keeps up the capability of hypothesizing and is becoming important. The performance metrics of three algorithms, Levenberg Marquardt algorithm, Resilient backpropagation algorithm, and Gradient Decent algorithm have been computed on Magnetic Resonance Imaging (MRI) images and it is observed that Levenberg Marquardt algorithm is more accurate when compared to the other two algorithms.

Keywords

Artificial Neural Network, Backpropagation Neural Network, Gradient Descent Algorithm (GD), Image Compression, Levenberg Marquardt Algorithm (LM), Resilient Backpropagation Algorithm (RP).

How to Cite this Article?

Kumari, V.G., Rao, S.G., and Rao, p.B., (2018). LM, RP AND GD Based Ann Architecture Models For Bio Medical Image Compression. i-manager’s Journal on Image Processing, 5(3), 21-33. https://doi.org/10.26634/jip.5.3.15195

References

[1]. Ahamed, S. A., & Chandrashekarappa, K. (2014). ANN implementation for image compression and decompression using back propagation technique. International Journal of Science and Research (IJSR), 3(6), 1848-1851.
[2]. Charif, H. N., & Salam, F. M. (2000). Neural networks based image compression system. Proc. 43rd IEEE Midwest Symposium on Circuits and Systems.
[3]. Dony, R. D., & Haykin, S. (1995). Neural network approaches to image compression. Proceedings of the IEEE, 83(2), 288-303.
[4]. Liu, L. (2007). The progress and analysis of image compression based on BPANN. Microcomputer Information, 23(6), 312-314.
[5]. Patel, B. K., & Agrawal, S. (2013). Image compression techniques using Artificial Neural Network. International Journal of Advanced Research in Computer Engineering & Technology, 2(10), 2725-2729.
[6]. Srivastava, R., & Singh, O. P. (2015). Lossless image compression using neural network. International Journal of Remote Sensing & Geoscience (IJRSG), 4(3), 39-43.
[7]. Veisi, H. & Jamzad, M. (2009). A Complexity-Based approach in Image Compression using Neural Networks. International Journal of Computer and Information Engineering, 3(11), 2619-2629.
[8]. Wilamowski, B. M., Chen, Y., & Malinowski, A. (1999). Efficient algorithm for training neural networks with one hidden layer. In IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No. 99CH36339) (Vol. 3, pp. 1725-1728). IEEE.
[9]. Xu, W., Nandi, A. K., & Zhang, J. (2003). Novel fuzzy reinforced learning vector quantisation algorithm and its application in image compression. IEE Proceedings- Vision, Image and Signal Processing, 150(5), 292-298.
[10]. Younis, M. C., & Khalil, R. A. (2006). Image Compression Technique Using a Hierarchical Neural Network. AL-Rafidain Journal of Computer Sciences and Mathematics, 3(2), 99-112.

Purchase Instant Access

Single Article

North Americas,UK,
Middle East,Europe
India Rest of world
USD EUR INR USD-ROW
Pdf 35 35 200 20
Online 35 35 200 15
Pdf & Online 35 35 400 25

If you have access to this article please login to view the article or kindly login to purchase the article
Options for accessing this content:
  • If you would like institutional access to this content, please recommend the title to your librarian.
    Library Recommendation Form
  • If you already have i-manager's user account: Login above and proceed to purchase the article.
  • New Users: Please register, then proceed to purchase the article.