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A Quantitative Comparative Study of Analytical and Iterative Reconstruction Techniques
Shrinivas D Desai, Linganagouda Kulkarni
Pages - 307 - 319     |    Revised - 30-08-2010     |    Published - 30-10-2010
Volume - 4   Issue - 4    |    Publication Date - October 2010  Table of Contents
image reconstruction, analytical, iterative, quantitative, computed tomography, SBP, FBP, ART
A special image restoration problem is the reconstruction of image from projections – a problem of immense importance in medical imaging, computed tomography and non-destructive testing of objects. This is a problem where a two – dimensional (or higher) object is reconstructed from several one –dimensional projections [1]. The reconstruction techniques are broadly classified into three categories, analytical, iterative, and statistical [2]. The comparative study among these is of great importance in the field of medical imaging. This paper aims at comparative study by analyzing quantitatively the quality of image reconstructed by analytical and iterative techniques. Projections (parallel beam type) for the reconstruction are calculated analytically by defining Shepp logan phantom head model with coverage angle ranging from 0 to ±180o with rotational increment of 2o to 10o. For iterative reconstruction coverage angle of ±90o, iteration up to 10 is used. The original image is grayscale image of size 128 X 128. The Image quality of the reconstructed image is measured by six quality measurement parameters. In this paper as analytical technique; simple back projection and filtered back projection are implemented, while as iterative; algebraic reconstruction technique is implemented. Experiment result reveals that quality of reconstructed image increase as coverage angle, and number of views increases. The processing time is one major deciding component for reconstruction. Keywords: Reconstruction algorithm, Simple-Back projection algorithm (SBP), Filter-Back projection algorithm (FBP), Algebraic Reconstruction Technique algorithm (ART), Image quality, coverage angle, Computed tomography (CT).
CITED BY (12)  
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2 Vadali, S., Deekshitulu, G. S., & Murthy, J. V. R. (2016). Optimization of Hyperspectral Images and Performance Evaluation Using Effective Loss Algorithm. In Proceedings of Fifth International Conference on Soft Computing for Problem Solving (pp. 919-927). Springer Singapore.
3 Chetih, N., & Messali, Z. (2015, May). Tomographic image reconstruction using filtered back projection (FBP) and algebraic reconstruction technique (ART). In Control, Engineering & Information Technology (CEIT), 2015 3rd International Conference on (pp. 1-6). IEEE.
4 Messali, Z., Chetih, N., Serir, A., & Boudjelal, A. (2015). A Quantitative Comparative Study of Back Projection, Filtered Back Projection, Gradient and Bayesian Reconstruction Algorithms in Computed Tomography (CT). International Journal of Probability and Statistics, 4(1), 12-31.
5 Nabil, C. H. E. T. I. H. (2014). La Méthode Descente De Gradient Pour La Reconstruction Tomographique Des Images 2D A Rayon-X. Journal of Advanced Research in Science and Technology, 1(2), 39-47.
6 Chetih, N., Messali, Z., & Serir, A. (2014). Comparaison entre la Rétro-Projection Filtrée (RPF) et l'Approche Bayésienne (AB) dans la Reconstruction Tomographique 2D. In International Conference on NDT and Material Industry and Alloys (IC-WNDT-MI'14).
7 Nair, P. C., & Suganthi, G. Comparative Analysis of Various Denoising Techniques for MRI Images.
8 Saha, S., Tahtali, M., Lambert, A., & Pickering, M. (2013, December). Perceptual dissimilarity metric: A full reference objective image quality measure to quantify the degradation of perceptual image quality. In Signal Processing and Information Technology (ISSPIT), 2013 IEEE International Symposium on (pp. 000327-000332). IEEE.
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Mr. Shrinivas D Desai
B V B College of Engineering & Technology - India
Dr. Linganagouda Kulkarni
Jayaprakash Narayan College of Engineering - India

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