Abstract
The overall aim of the research is to compare the retrieved image with the original image with respect to the evaluation factors such as MAE, MSE, PSNR and RMSE which reflects the quality of biomedical image for telemedicine with minimum percentage of error at the recipient side. This paper presents spectral coding technique for biomedical images using neural networks in-order to accomplish the above objectives. This work is in continuity of ongoing research project aimed at developing a system for efficient image compression approach for telemedicine in Saudi Arabia. This work compares the efficiency of proposed technique against existing image compression techniques viz JPEG2000 and improved BPNN. To my knowledge, the research is the primary in providing a comparative study of evaluation factors with other techniques used in compression of biomedical images. This work is explored and tested on biomedical images such as X-rays, CT, MRI, PET etc.
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References
Wallace, G.K.: The JPEG still picture compression standard. IEEE Trans. on Cons. Elec. 38(1) (1992)
Usevitch, B.E.: A tutorial on modern lossy wavelet image compression: Foundations of JPEG 2000. IEEE Sig. Proc. Mag. 18(5), 22–35 (2001)
Jyotheswar, J., Mahapatra, S.: Efficient FPGA implementation of DWT and modified SPIHT for lossless image compression. J. of Sys. Arch. 53(7), 369–378 (2007)
Taubman, D.: High performance scalable image compression with EBCOT. IEEE Trans. on Img. Proc. 9(7), 1158–1170 (2000)
Srikala, P., Umar, S.: Neural network based image compression with lifting scheme and RLC. Int. J. of Res. Engg. and Tech. 1(1), 13–19 (2012)
Matsuoka, R., Sone, M., Fukue, K., Cho, K., Shimoda, H.: Quantitative Analysis of Image Quality Of Lossy Compression Images. International Society for Photogrammetry and Remote Sensing (2013), http://www.isprs.org/proceedings/XXXV/congress/comm3/papers/348.pdf
Chang, S.G., Yovanof, G.S.: A simple block-based lossless image compression scheme. Proc. of the 13th Asilomar Conf. on Sig., Sys. and Comp. 1, 591–595 (1996)
Chen, Y.T., Tseng, D.C., Chang, P.C.: Wavelet-based medical image compression with adaptive prediction. In: Proc. of the Int. Symp. on Intelli. Sig. Proc. and Comm. Sys. (ISPACS 2005), pp. 825–828 (2005)
Spires, W.: Lossless Image Compression Via the Lifting Scheme (2005), http://www.cs.ucf.edu/~wspires/lossless_img_lifting.pdf
Laura, L., Camacho, M.T.V., Badran, A., Armando, D.G.: Images Compression for Medical Diagnosis Using Neural Networks (1990), http://journal.info.unlp.edu.ar/journal/journal2/papers/image.pdf
Yeo, W.K., Yap, D.F.W., Oh, T.H., Andito, D.P., Kok, S.L., Ho, Y.H., Suaidi, M.K.: Grayscale medical image compression using feedforward neural networks. In: Proceedings of the IEEE Conf. on Comp. Appl. and Ind. Elec (ICCAIE 2011), pp. 633–638 (2011)
Durai, S.A., Saro, E.A.: Image compression with backpropagation neural network using cumulative distribution function. Int. J. of Engg. and Appl. Sci. 3(4), 185–189 (2007)
Wan, T.C., Kabuka, M.: Edge preserving image compression for magnetic resonance Images using DANN-based neural networks. In: Med. Img. Proc. of SPIE, vol. 2164 (1994)
Liang, J.-Y., Chen, C.-S., Huang, C.-H., Liu, L.: Lossless compression of medical images using Hilbert space-filling curves. Comp. Med. Img. and Grap. 32(3), 174–182 (2008)
Khashman, A., Dimililer, K.: Medical radiographs compression using neural networks and haar wavelet. In: Proc. of the IEEE EUROCON 2009, pp. 1448–1453 (2009)
Northan, B., Dony, R.D.: Image compression with a multiresolution neural network. Canadian J. of Electrical and Comp. Engg. 31(1), 49–58 (2006)
Mi, J., Huang, D.: Image compression using principal component neural network. In: Proc. of the 8th Int. Conf. on Cont., Auto., Robo. and Vis., pp. 698–701 (2008)
Kulkarni, S., Verma, B., Blumenstein, M.: Image compression using a direct solution method based neural network. In: Proc. of the 10th Australian Joint Conf. on Arti. Intelli., pp. 114–119 (1997)
Cottrell, G., Munro, P., Zipser, D.: Image Compression by Back Propagation: An Example of Extensional Programming. Adv. in Cogn. Sci. (1989)
Khashman, A., Dimililer, K.: Neural networks arbitration for optimum DCT image compression. In: Proc. of the Int. Conf. on Comp. as a Tool (EUROCON 2007), pp. 151–156 (2007)
Ma, L., Khorasani, K.: Adaptive constructive neural networks using hermite polynomials for image compression. In: Wang, J., Liao, X.-F., Yi, Z. (eds.) ISNN 2005. LNCS, vol. 3497, pp. 713–722. Springer, Heidelberg (2005)
Karlik, B.: Medical image compression by using vector quantization neural network (VQNN). Neu. Net.World 16(4), 341–348 (2006)
Zhou, Y., Zhang, C., Zhang, Z.: Improved variance-based fractal image compression using neural networks. In: Wang, J., Yi, Z., Żurada, J.M., Lu, B.-L., Yin, H. (eds.) ISNN 2006. LNCS, vol. 3972, pp. 575–580. Springer, Heidelberg (2006)
Tripathi, P.: Image compression enhancement using bipolar coding with LM algorithm in artificial neural network. Int. J. of Sci. and Res. Publications 2(8) (2012)
Rajput, G.G., Singh, M.K.: Modeling of neural image compression using GA and BP: a comparative approach. Int. J. of Adv. Comp. Sci. and Appl., 26–34 (2011)
Gaidhane, V., Singh, V., Kumar, M.: Image compression using PCA and improved technique with MLP neural network. In: Proc. of the 2nd Int. Conf. on Adv. in Recent Technologies in Comm. and Computing (ARTCom 2010), pp. 106–110 (2010)
Laha, A., Pal, N.R., Chanda, B.: Design of vector quantizer for image compression using self-organizing feature map and surface fitting. IEEE Trans. on Img. Proc. 13(10), 1291–1303 (2004)
AL-Allaf, O.N.A.: Improving the performance of backpropagation neural network algorithm for image compression/ decompression system. J. of Comp. Sci. 6(11), 1347–1354 (2010)
Lin, C.-T., Fan, K.-W., Pu, H.-C., Lu, S.-M., Liang, S.-F.: An HVS-directed neural-network-based image resolution enhancement scheme for image resizing. IEEE Trans. on Fuzzy Sys. 15(4), 605–615 (2007)
Hui, G., Yongxue, W.: Wavelet packet and neural network basismedical image compression. In: Proc. of the Int. Conf. on E-Product E-Service and E-Entertainment, pp. 1–3 (2010)
Rekha, S.V.: A segmented wavelet inspired neural network approach to compress images. IOSR J. of Comp. Engg. 2(6), 36–42 (2012)
Mishra, A., Zaheeruddin, Z.: Hybrid fuzzy neural network based still image compression. In: Proc. of the Int. Conf. on Computational Intelli. and Comm. Net. (CICN 2010), pp. 116–121 (2010)
Vijideva, R.: Neural network-wavelet based dicom image compression and progressive transmission. Int. J. of Engg. Sci. & Adv. Tech. 2(4), 702–710 (2012)
Obiegbu, C.: Image compression using cascaded neural networks (M.S. thesis) (2003), http://scholarworks.uno.edu
Senthilkumaran, N., Suguna, J.: Neural network technique for lossless image compression using X-ray images. Int. J. of Comp. and Elec. Engg. 3(1) (2011)
Saudagar, A.K.J., Shathry, O.A.: Neural network based image compression approach to improve the quality of biomedical image for telemedicine. British J. of Applied Sci. and Tech. 4(3), 510–524 (2014)
Saudagar, A.K.J.: Minimize the Percentage of Noise in Biomedical Images Using Neural Networks. The Scient. World J. 2014, Article ID 757146
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Jilani Saudagar, A.K. (2015). A Case Study of Evaluation Factors for Biomedical Images Using Neural Networks. In: Satapathy, S., Biswal, B., Udgata, S., Mandal, J. (eds) Proceedings of the 3rd International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA) 2014. Advances in Intelligent Systems and Computing, vol 327. Springer, Cham. https://doi.org/10.1007/978-3-319-11933-5_27
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DOI: https://doi.org/10.1007/978-3-319-11933-5_27
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