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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
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KEYWORDS
image reconstruction, analytical, iterative, quantitative, computed tomography, SBP, FBP, ART
ABSTRACT
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)  
1 Babu, K. S., Thyagharajan, K. K., & Ramachandran, V. (2016). Compression of Hyper-Spectral Images and Its Performance Evaluation. In Proceedings of the 4th International Conference on Frontiers in Intelligent Computing: Theory and Applications (FICTA) 2015 (pp. 599-609). Springer India.
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.
9 Saha, S., Liu, S., Tahtali, M., Lambert, A., & Pickering, M. (2013, June). Perceptual image quality: A dissimilarity measure to quantify the degradation of image quality. In Visual Information Processing (EUVIP), 2013 4th European Workshop on (pp. 245-249). IEEE.
10 De Smedt, T. Dosisanalyse van iteratieve reconstructiealgoritmes voor.
11 Al-Ameen, Z., Sulong, G., & Johar, M. G. M. (2012). Fast deblurring method for computed tomography medical images using a novel kernels set. International Journal of Bio-Science and Bio-Technology, 4(3), 9-19.
12 Saha, S., Tahtali, M., Lambert, A., & Pickering, M. (2012, December). Perceptual Dissimilarity: A Measure to Quantify the Degradation of Medical Images. In Digital Image Computing Techniques and Applications (DICTA), 2012 International Conference on (pp. 1-6). IEEE.
1 Google Scholar 
2 Academic Index 
3 CiteSeerX 
4 refSeek 
5 iSEEK 
6 Socol@r  
7 Scribd 
8 SlideShare 
9 PDFCAST 
10 PdfSR 
A. B. Watson. “DCTune: A technique for visual optimization of DCT quantization matrices for individual images”. Soc. Inf. Display Dig. Tech. Papers, vol. XXIV, pp. 946–949, (1993)
A. K. Jain. “Fundamentals of Digital Image Processing”, Prentice – Hall of India, 1 - Edition, pp.431 – 470, (1989)
A.M. Ali, Z. Melegy , M. Morsy , R.M. Megahid , T. Bucherl , E.H. Lehmann. “Image reconstruction techniques using projection data from transmission method”. Annals of Nuclear Energy, 31: 1415–1428, Elsevier Ltd, 2004
CIPIC PQS ver. 1.0, [Online]. Available at: http://msp.cipic.ucdavis.edurestes/ftp/cipic/code/pqs,
D. V. Weken, M. Nachtegael, E. E. Kerre. “Using similarity measures and homogeneity for the comparison of images”. Image Vis. Comput., 22: 695–702, 2004
Gordon, R., R. Bender, G.T. Herman. “Algebraic reconstruction techniques (ART) for threedimensional electron microscopy and x- ray photography”. Journal of Theoretical Biology, 29:471-481, 1970.
H. R. Sheikh, A. C. Bovik, G. de Veciana. “An information fidelity criterion for image quality assessment using natural scene statistics”. IEEE Trans. Image Process., 14(12): 2117–2128, 2005
H. R. Sheikh, A. C. Bovik. “Image information and visual quality”. IEEE Trans. Image Process., 15(2), 430–444, 2006
H. R. Sheikh, M. F. Sabir, A. C. Bovik. “A Statistical Evaluation of Recent Full Reference Image Quality Assessment Algorithms”. IEEE Transactions on Image Processing, 15(11): 2006
I. Avcibas¸, B. Sankur, K. Sayood. “Statistical evaluation of image quality measures”. J. Electron. Imag., 11(2): 206–23, 2002.
J. Lubin. “A visual discrimination mode for image system design and evaluation”. in Visual Models for Target Detection and Recognition (E. Peli, ed.), Singapore: World Scientific Publishers pp. 245–283, (1995)
JNDmetrix Technology Sarnoff Corp., evaluation Version, 2003 [Online]. Available at : http://www.sarnoff.com/products-services/video-vision/jndmetrix/ downloads.asp
K. B. Raja, M.Madheswaran, K.Thyagarajah. “Quantitative and Qualitative Evaluation of US Kidney Images for Disorder Classification using Multi-Scale Differential Features”. ICGST-BIME Journal, 7(1): 2007
K. Mueller and R. Yagel. “On the Use of Graphics Hardware to Accelerate Algebraic Reconstruction Methods”. Presented at the SPIE Medical Imaging Conference Physics of Medical Imaging, San Diego, 1999
Kaczmarz, S. „Angenäherte Auflösung von Systemen linearer Gleichungen“. Bulletin International de l'Academie Polonaise des Sciences et des Lettres, series A, 35 :335- 357, 1937
M. Miyahara, K. Kotani, V. R. Algazi,. “Objective Picture Quality Scale (PQS) for image coding,” IEEE Trans. Commun., 46(9), 1215–1225,1998
Medical Image Samples Sebastien Barre, Online image database, “Archive of DICOM images” [online] Available at:http://www.barre.nom.fr/medical/samples/
N. Damera-Venkata, T. D. Kite, W. S. Geisler, B. L. Evans, A.C. Bovik. “Image quality assessment based on a degradation model”. IEEE Trans. Image Process., 4(4): 636– 650, 2000
N. Yamsang and S. Udomhunsakul. “Image Quality Scale (IQS) for Compressed Images Quality Measurement”. Proceedings of the International Multi Conference of Engineers and Computer Scientists 2009 Vol I IMECS 2009, Hong Kong, 2009
P. P. Bruyant. “Analytic and Iterative Reconstruction Algorithms in SPECT”. Journal of Nuclear Medicine, by Society of Nuclear Medicine, 43(10): 1343-1358, 2002
S. D. Desai. “Reconstruction of image from projections-an application to MRI & CT Scanning”. In proceedings of International conference ICSCI-2005
Z. Wang, A. C. Bovik, H. R. Sheikh, E. P. Simoncelli. “Image Quality Assessment: From Error Visibility to Structural Similarity”, IEEE Transactions On Image Processing, 13(4): 2004
Z. Wang, A. C. Bovik. “A universal image quality index”. IEEE Signal Processing Letters, 9: 81–84, 2002.
Z. Wang, E. P. Simoncelli, and A. C. Bovik, “Multi-scale structural similarity for image quality assessment,” presented at the IEEE Asilomar Conf. Signals, Systems, and Computers, 2003
Mr. Shrinivas D Desai
B V B College of Engineering & Technology - India
sd_desai@bvb.edu
Dr. Linganagouda Kulkarni
Jayaprakash Narayan College of Engineering - India


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