CFP last date
20 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

Superior Reconstruction Quality Improvement of CT Image for Bias Correction Variance Measures

by S.asif Hussain, M.n.giri Prasad,, D. Satyanarayana
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 47 - Number 5
Year of Publication: 2012
Authors: S.asif Hussain, M.n.giri Prasad,, D. Satyanarayana
10.5120/7185-9918

S.asif Hussain, M.n.giri Prasad,, D. Satyanarayana . Superior Reconstruction Quality Improvement of CT Image for Bias Correction Variance Measures. International Journal of Computer Applications. 47, 5 ( June 2012), 22-29. DOI=10.5120/7185-9918

@article{ 10.5120/7185-9918,
author = { S.asif Hussain, M.n.giri Prasad,, D. Satyanarayana },
title = { Superior Reconstruction Quality Improvement of CT Image for Bias Correction Variance Measures },
journal = { International Journal of Computer Applications },
issue_date = { June 2012 },
volume = { 47 },
number = { 5 },
month = { June },
year = { 2012 },
issn = { 0975-8887 },
pages = { 22-29 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume47/number5/7185-9918/ },
doi = { 10.5120/7185-9918 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:41:06.716407+05:30
%A S.asif Hussain
%A M.n.giri Prasad,
%A D. Satyanarayana
%T Superior Reconstruction Quality Improvement of CT Image for Bias Correction Variance Measures
%J International Journal of Computer Applications
%@ 0975-8887
%V 47
%N 5
%P 22-29
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Image segmentation algorithms based on ROI typically rely on the homogeneity of image intensities. CT scanner is dedicated as research Scanner which has been developed in view of imaging applications. A key Feature of the work is to use Empirical system to achieve resolution recovery with novel region based method. This method identifies local intensity cluster with local clustering criterion function with respect to neighborhood center. Reconstruction quality is analyzed quantitatively in terms bias field correction for intensity inhomogenity correction. This method is valid on synthetic images of various imaging modalities. A significant improvement in reconstruction quality can be realized by faster and more accurate visual quality quantitative measures where Reconstruction quality is analyzed quantitatively in terms of bias-variance measures (bar phantom) and mean square error (lesion phantom). However, with the inclusion of the empirical kernel, the iterative algorithms provide superior reconstructions compared to FBP, both in terms of visual quality and quantitative measures. Simulated results show improved tumor bias and variance characteristics with the proposed algorithm.

References
  1. Olabarriaga,S. D. and Smeulders, A. W. M. , "Interaction in the Segmentation of Medical Images: A Survey",Med. Image Analysis, 5: 127-142, 2001.
  2. PN. T. Wells and M. Halliwell, "Speckle in ultrasonic imaging," Ultrasonic's, vol. 19, pp. 225–229, 1981.
  3. R. F. Wagner, S. W. Smith, J. M. Sandrik, and H. Lopez, "Statistics of speckle in ultrasound B-scans," IEEE Trans. Sonics Ultrasonic's, vol. SU-30, pp. 156–163, Mar. 1983.
  4. J. C. Bamber and C. Daft, "Adaptiv filtering for reduction of speckle in ultrasonic pulse-echo images," Ultrasonic Image. , pp. 41–44, January 1986.
  5. G. Castellini, D. Lamate, L. Mascotti, E. Monnini, and S. Rocchi, "An adaptive Kalman filter for speckle reduction in ultrasound images," J. Nucl. Med. Appl. Sci. , vol. 32, no. 3, p. 213, 1988.
  6. D. Kaplan and Q. Ma, "On the statistical characteristics of log-compressed Rayleigh signals: Theoretical formulation and experimental results," J. Acoust. Soc. Amer. , vol. 95, no. 3, pp. 1396–1400, 1994.
  7. V. Dutt and J. F. Greenleaf, "Adaptive speckle reduction filter for log-compressed B-scan images," IEEE Trans. Med. Image. , vol. 15, pp. 802–813, Dec. 1996.
  8. A. N. Evans and M. S. Nixon, "Biased motion-adaptive temporal filtering for speckle-reduction in echocardiography," IEEE Trans. Med. Image. , vol. 15, pp. 39–50, Feb. 1996.
  9. T. Chan and L. Vese, "Active contours without edges," IEEE Trans. Image. Process. vol. 10, no. 2, pp. 266–277, Feb. 2001.
  10. C. Li, C. Kao, J. C. Gore, and Z. Ding, "Minimization of region-scalable fitting energy for image segmentation," IEEE Trans. Image Process. , vol. 17, no. 10, pp. 1940–1949, Oct. 2008.
  11. V. Caselles, R. Kimmel, and G. Sapiro, "Geodesic active contours," Int. J. Comput. Vis. , vol. 22, no. 1, pp. 61–79, Feb. 1997.
  12. S. Kichenassamy, A. Kumar, P. Olver, A. Tannenbaum, and A. Yezzi, "Gradient flows and geometric active contour models," in Proc. 5th Int. Conf. Comput. Vis. , 1995, pp. 810–815.
  13. R. Kimmel, A. Amir, and A. Bruckstein, "Finding shortest paths on surfaces using level set propagation," IEEE Trans. Pattern Anal. Mach. Intell. , vol. 17, no. 6, pp. 635–640, Jun. 1995.
  14. R. Malladi, J. A. Sethian, and B. C. Vemuri, "Shape modeling with front propagation: A level set approach," IEEE Trans. Pattern Anal. Mach. Intell. , vol. 17, no. 2, pp. 158–175, Feb. 1995.
  15. R. Ronfard, "Region-based strategies for active contour models," Int. J. Comput. Vis. , vol. 13, no. 2, pp. 229–251, Oct. 1994.
  16. C. Samson, L. Blanc-Feraud, G. Aubert, and J. Zerubia, "A variational model for image classification and restoration," IEEE Trans. Pattern Anal. Mach. Intell. , vol. 22, no. 5, pp. 460–472, May 2000.
  17. S. Theodoridis and K. Koutroumbas, Pattern Recognition. New York: Academic, 2003.
  18. A. Tsai, A. Yezzi, and A. S. Willsky, "Curve evolution implementation of the Mumford-Shah functional for image segmentation, de noising, interpolation, and magnification," IEEE Trans. Image Process. , vol. 10, no. 8, pp. 1169–1186, Aug. 2001.
  19. A. Vasilevskiy and K. Siddiqi, "Flux-maximizing geometric flows," IEEE Trans. Pattern Anal. Mach. Intell. , vol. 24, no. 12, pp. 1565–1578, Dec. 2002.
  20. L. Vese and T. Chan, "A multiphase level set framework for image segmentation using the Mumford and Shah model," Int. J. Comput. Vis. , vol. 50, no. 3, pp. 271–293, Dec. 2002.
  21. N. Paragios and R. Deriche, "Geodesic active regions and level set methods for supervised texture segmentation," Int. J. Comput. Vis. , vol. 46, no. 3, pp. 223–247, Feb. 2002.
  22. S. Theodoridis and K. Koutroumbas, Pattern Recognition. NewYork:Academic, 2003.
  23. S. Asif Hussain and Dr. M. N. Giri Prasad, Dr, D. Satya Narayana "3d Medical image modeling with dynamic image reconstruction "in IJERT Vol 5, No. 3 (2012) PP. 213-217
Index Terms

Computer Science
Information Sciences

Keywords

Intensity Inhomogeneities Empirical System Kernel Bias-variance Iterative Algorithms

Powered by PhDFocusTM