Investigating Polynomial Interpolation Functions for Zooming Low Resolution Digital Medical Images
Authors: Maninder Pal
Abstract:
Medical digital images usually have low resolution because of nature of their acquisition. Therefore, this paper focuses on zooming these images to obtain better level of information, required for the purpose of medical diagnosis. For this purpose, a strategy for selecting pixels in zooming operation is proposed. It is based on the principle of analog clock and utilizes a combination of point and neighborhood image processing. In this approach, the hour hand of clock covers the portion of image to be processed. For alignment, the center of clock points at middle pixel of the selected portion of image. The minute hand is longer in length, and is used to gain information about pixels of the surrounding area. This area is called neighborhood pixels region. This information is used to zoom the selected portion of the image. The proposed algorithm is implemented and its performance is evaluated for many medical images obtained from various sources such as X-ray, Computerized Tomography (CT) scan and Magnetic Resonance Imaging (MRI). However, for illustration and simplicity, the results obtained from a CT scanned image of head is presented. The performance of algorithm is evaluated in comparison to various traditional algorithms in terms of Peak signal-to-noise ratio (PSNR), maximum error, SSIM index, mutual information and processing time. From the results, the proposed algorithm is found to give better performance than traditional algorithms.
Keywords: Zooming, interpolation, medical images, resolution.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1123636
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1575References:
[1] H. Singh, J. A. Neutze, J. R. Enterline, J. S. Fotos, J. J. Douds, M. J. K. and M. J. Bluto, “Radiology Fundamentals Introduction to Imaging & Technology”, Fourth Edition Springer, LLC 2012.
[2] W. A. Kalender, “X-ray computed tomography”, Institute of Physics Publishing Physics in Medicine and Biology Phys. Med. Biol, vol. 51, pp. R29–R43, 2006.
[3] T. Geva, “Magnetic Resonance Imaging: Historical Perspective”, Journal of Cardiovascular Magnetic Resonance, vol. 8, pp. 573-580, 2006.
[4] Y. I. T. Gerbrands, J. J. V. Vliet, L. Jozef, “Fundamentals of Image Processing”, Delft University of Technology, pp.1-112, 1995.
[5] J. Verdera, “Some Interpolation Problems in Image Processing”, PhD Thesis, Pompeu Fabra University Barcelona, Spain, November 2003.
[6] I. P. Thévenaz, T. Blu and M. Unser, “Interpolation Revisited”, IEEE Trans. Medical Imaging, vol. 19, no. 7, pp. 739-758, July 2000.
[7] F. Xu, H. Liu, G. Wang and B. A. Alford “Comparison of adaptive linear interpolation and conventional interpolation for digital radiography systems,” Journal of Electronic Imaging, vol. 9, no. 1, pp. 22-31, January 2000.
[8] R. Lu, P. Marziliano and C. H. Thng, “Comparison of scene-based interpolation methods applied to CT abdominal images,” presented at the 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEMBS’ 04, vol.1, pp.1561-1564, 1-5 Sept. 2004.
[9] I. M. Unser, “Splines: A Perfect Fit for Signal and Image Processing,” IEEE Signal Processing Magazine, vol. 16, no. 6, pp. 22-38, 1999.
[10] J. A. Parker, R. V. Kenyon, and D. E. Troxel, “Comparison of Interpolating Methods for Image Resampling,” IEEE transactions on Medical Imaging, vol. MI-2, No. 1, March 1983.
[11] Y. Tamura, Tanaka and Kiyoshi, “Image enlargement using bi-directional shifted linear interpolation,” International Symposium on Intelligent Signal Processing and Communications Systems, ISPACS 2008, pp.1-4, 8-11 Feb. 2009.
[12] R. C. Gonzalez, R. E. Woods, “Digital Image Processing (2nd Edition)”, Prentice Hall; 2nd edition, January 15, 2002.
[13] I.J Schoenberg, “Cardinal interpolation and spline functions: II interpolation of data of power growth”, Journal of Approximation Theory, vol. 6 (4), pp. 404-420, December 1972.
[14] T. M. Lehmann, C. Gonner and K. Spitzer, “Addendum: B-spline interpolation in medical image processing,” IEEE Transactions on Medical Imaging, vol.20, No.7, pp.660-665, July 2001.
[15] W. C. Siu and K. W. Hung, “Review of image interpolation and super-resolution,” presented at the Signal & Information Processing Association Annual Summit and Conference (APSIPA ASC), 2012 Asia-Pacific, pp.1-10, 3-6 Dec. 2012.
[16] Y. Sa, “Improved Bilinear Interpolation Method for Image Fast Processing,” presented at the 7th International Conference on Intelligent Computation Technology and Automation 2014, pp.308-311, 25-26 Oct. 2014.
[17] Y. Hu, W. L. Chen and J. R. Zeng, “Adaptive Image Zooming Based on Bilinear Interpolation and VQ Approximation”, Signal Processing, Image Processing and Pattern Recognition Communications in Computer and Information Science, vol. 260, pp. 310-319, 2011.
[18] R. Roy, M. Pal and T. Gulati, “Zooming Digital Images using Interpolation Techniques”, International Journal of Application or Innovation in Engineering & Management, vol. 2, Issue 4, April 2013.
[19] T. M. Lehmann, C. Gonner and K. Spitzer, “Survey: Interpolation methods in medical image processing,” IEEE Transactions on Medical Imaging, vol.18, No.11, pp.1049-1075, Nov. 1999.
[20] J. W. Hwang and H. S. Lee, “Adaptive image interpolation based on local gradient features,” IEEE Signal Processing Letters, vol.11, No.3, pp.359-362, March 2004.
[21] J. Li, M. Xin and J. Jin, “An Evolutionary approach for Gray-level Image Zooming,” NASA/ESA Conference on Adaptive Hardware and Systems (AHS), 2011, pp.383-389, 6-9 June 2011.
[22] Y. Xiao, Y. Pang and L. Zhao, “Image Zooming Method with Hierarchical Structure,” presented at the 2013 International Conference on Information Science and Cloud Computing Companion, pp.787-792, 7-8 Dec. 2013.
[23] L. Shufeng and S. Shaohong, “An Adaptive Image Interpolation Method Focusing on Edge Information,” presented at the 2014 Seventh International Symposium on Computational Intelligence and Design (ISCID), vol.1, pp.421-424, 13-14 Dec. 2014.
[24] Z. Mai, J. Rajan, M. Verhoye, and J. Sijbers, “Robust edge-directed interpolation of magnetic resonance images,” presented at the 4th International Conference on Biomedical Engineering and Informatics (BMEI), 2011, vol.1, pp.472-476, 15-17 Oct. 2011.
[25] X. Liang, M. T. Orchard, “New Edge-Directed Interpolation”, IEEE Transactions On Image Processing, vol. 10, No. 10, October 2001.
[26] Z. Dengwen, “An edge-directed bicubic interpolation algorithm,” presented at the 3rd International Congress on Image and Signal Processing (CISP), 2010, vol.3, pp.1186-1189, 16-18 Oct. 2010.
[27] W. S. Tam, C. W. Kok and W. C. Siu, “A modified edge directed interpolation for images”, presented at the 17th European Signal Processing Conference (EUSIPCO 2009), Glasgow, Scotland, August 24-28, 2009.
[28] X. Zhang, S. Ma, Y. Zhang, L. Zhang and W. Gao, “Nonlocal Edge-Directed Interpolation”, Advances in Multimedia Information Processing - PCM 2009, Lecture Notes in Computer Science, vol. 5879, pp 1197-1207, 2009.
[29] W. S. Tam, C. W. Kok and W. C. Siu, “Modified edge-directed interpolation for images”, Journal of Electronic Imaging, vol. 19, Issue 1, January 2010.
[30] S. Yu, Q. Zhu, S. Wu and Y. Xie, “Performance Evaluation of Edge‐Directed Interpolation Methods for Images”, Computer Vision and Pattern Recognition, 2013.
[31] A. A. Abdelwahab, M. K. Ahmed and S. H. Hashem, “Image Enhancement Using a Contrast measure in the Discrete Wavelet Transform”, presented at the 24th National Radio Science Conference, March 13-15, 2007.
[32] G. Anbarjafari and H. Demirel, “Image Super Resolution Based on Interpolation of Wavelet Domain High Frequency Subbands and the Spatial Domain Input Image”, ETRI Journal, vol. 32, Issue 3, pp. 390-395, Jun 2010.
[33] H. Demirel, C. Ozcinar, and G. Anbarjafari, “Satellite Image Contrast Enhancement Using Discrete Wavelet Transform and Singular Value Decomposition”, IEEE Geoscience and Remote Sensing Letters, vol. 7, No. 2, April 2010.
[34] H. Demirel and G. Anbarjafari, “Satellite Image Resolution Enhancement Using Complex Wavelet Transform”, IEEE Geoscience and Remote Sensing Letters, vol. 7, No. 1, January 2010.
[35] H. Demirel and G. Anbarjafari, “IMAGE Resolution Enhancement by Using Discrete and Stationary Wavelet Decomposition”, IEEE Transactions on Image Processing, vol. 20, No. 5, May 2011.
[36] H. Demirel and G. Anbarjafari, “Discrete Wavelet Transform-Based Satellite Image Resolution Enhancement”, IEEE Transactions On Geoscience and Remote Sensing, vol. 49, No. 6, June 2011.
[37] T. Celik and T. Tjahjadi, “Image Resolution Enhancement Using Dual-Tree Complex Wavelet Transform”, IEEE Geoscience And Remote Sensing Letters, vol. 7, No. 3, July 2010.