Call for Paper - May 2023 Edition
IJCA solicits original research papers for the May 2023 Edition. Last date of manuscript submission is April 20, 2023. Read More

Interpreting Low Resolution CT Scan Images using Interpolation Functions

International Journal of Computer Applications
© 2013 by IJCA Journal
Volume 74 - Number 3
Year of Publication: 2013
Tarun Gulati
Maninder Pal

Tarun Gulati and Maninder Pal. Article: Interpreting Low Resolution CT Scan Images using Interpolation Functions. International Journal of Computer Applications 74(3):50-58, July 2013. Full text available. BibTeX

	author = {Tarun Gulati and Maninder Pal},
	title = {Article: Interpreting Low Resolution CT Scan Images using Interpolation Functions},
	journal = {International Journal of Computer Applications},
	year = {2013},
	volume = {74},
	number = {3},
	pages = {50-58},
	month = {July},
	note = {Full text available}


This paper focuses on interpreting low resolution computed tomography (CT) scan medical images using interpolation functions. Image processing operations such as zooming and segmentation are very commonly performed on these images in medical sciences. However, it is very challenging to perform such operations because of poor resolution of these images. Over the last several years; significant improvements have been made in this area; however, it is still very challenging. In particularly, zooming of such images is very complicated. For zooming, the process of re-sampling is normally employed. Therefore, this paper focuses on investigating the effect of interpolation functions on zooming low resolution images. For this purpose, ideally, an ideal low-pass filter is preferred; however, the same is difficult to realize in practice. Therefore, four interpolation functions (nearest neighbor, linear, cubic B-spline and high-resolution cubic spline with edge enhancement (-2?a?0)) are investigated in this paper for the low resolution medical CT scan images. From the results, it is found that cubic B-spline and high-resolution cubic spline have a better frequency response than nearest neighbor and linear interpolation functions. When these functions are applied for the purpose of zooming digital images, the best response was obtained with the high-resolution cubic spline functions; however, at the expense of increase in computation time.


  • M. Hanumantharaju, M. Ravishankar, D. R. Rameshbabu & S. Ramachandran, "Color Image Enhancement using Multiscale Retinex with Modified Color Restoration Technique," Second International Conference on Emerging Applications of Information Technology (ICEAIT), pp. 93-97, 2011.
  • Z. Min, W. Jiechao, L. Zhiwei & L. Yonghua, "An Adaptive Image Zooming Method with Edge Enhancement," 3rd International Conference on Advanced Computer Theory and Engineering (ICACTE), pp. 608-611, 2010.
  • Wing-Shan Tam, C. W. Kok & W. C. Siu, "A Modified Edge Directed Interpolation For Images," 17th European Signal Processing Conference (ESPC), 2009.
  • W. Bender & C. Rosenberg, "Image Enhancement Using Non-uniform Sampling," Proc. SPIE-int. Soc. Opt. Eng. , Volume 1460, pp. 59-70, 1991.
  • P. Thvenaz, T. Blu & M. Unser, "Image Interpolation and Resampling," Handbook of Medical Imaging, Processing and Analysis, I. N. Bankman (eds. ), Academic Press, San Diego CA, USA, pp. 393-420, 2000.
  • M. Zamani, "An Applied Two-Dimensional B-spline Model for Interpolation of Data," International Journal of Advanced Research in Engineering and Technology (IJARET), Volume 3(2), July-December 2012. R. G. Keys, "Cubic convolution interpolation for digital image processing," IEEE Trans. Acoust. , Speech, Sig. Proc. , Volume 29(6), pp. 1153-1160, 1981.
  • R. W. Schafer & L. R. Rabiner, "A digital signal processing approach to interpolation," Proc. IEEE, Volume 61, pp. 692–702, 1973.
  • T. Acharya & P. S. Tsai, "Computational Foundations of Image Interpolation Algorithms," ACM Ubiquity Volume 8, 2007.
  • T. M. Lehmann, C. Gonner & K. Spitzer, "Interpolation Methods in Medical Image Processing," IEEE Transactions on Medical Imaging, Volume 18(11), pp. 1049-1075, 1999.
  • J. Anthony Parker, R. V. Kenyon & D. E. Troxel, "Comparison of Interpolating Methods for Image Resampling," IEEE Transactions on Medical Imaging, Volume 2(1), pp. 31-39, 1983.
  • E. Maeland, "On the comparison of the interpolation methods," IEEE Trans. Med. Imag. , Volume 7(3), pp. 213-217, 1988.
  • M. F. Fahmy, T. K. Abdel Hameed & G. F. Fahmy, "A Fast B-spline Based Algorithm for Image zooming and Compression," 24th National Radio Science Conference (NRSC), Egypt, 2007.
  • H. S. Hou & H. C. Andrews, "Cubic Splines for Image Interpolation and Digital Filtering," IEEE Trans. Acoust. , Speech, Signal Processing, Volume 26, pp. 508-517, 1978.
  • S. E. Reichenbach & S. K. Park, "Two-parameter cubic convolution for image reconstruction", Proc. SPIE, Volume 1199, pp. 833-840, 1989.
  • M. Unser, "A Perfect Fit for Signal/Image Processing: Splines," Proceedings of the SPIE International Symposium on Medical Imaging: Image Processing (MI'02), 4684, Part I, pp. 225-236, San Diego CA, 2002.