CFP last date
22 April 2024
Reseach Article

Kidney Segmentation from Ultrasound Images using Gradient Vector Force

Published on None 2010 by Arpana M. Kop, Ravindra Hegadi
Recent Trends in Image Processing and Pattern Recognition
Foundation of Computer Science USA
RTIPPR - Number 2
None 2010
Authors: Arpana M. Kop, Ravindra Hegadi
320bd86d-d566-49ea-b3a6-b7d441c51114

Arpana M. Kop, Ravindra Hegadi . Kidney Segmentation from Ultrasound Images using Gradient Vector Force. Recent Trends in Image Processing and Pattern Recognition. RTIPPR, 2 (None 2010), 104-109.

@article{
author = { Arpana M. Kop, Ravindra Hegadi },
title = { Kidney Segmentation from Ultrasound Images using Gradient Vector Force },
journal = { Recent Trends in Image Processing and Pattern Recognition },
issue_date = { None 2010 },
volume = { RTIPPR },
number = { 2 },
month = { None },
year = { 2010 },
issn = 0975-8887,
pages = { 104-109 },
numpages = 6,
url = { /specialissues/rtippr/number2/983-106/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Special Issue Article
%1 Recent Trends in Image Processing and Pattern Recognition
%A Arpana M. Kop
%A Ravindra Hegadi
%T Kidney Segmentation from Ultrasound Images using Gradient Vector Force
%J Recent Trends in Image Processing and Pattern Recognition
%@ 0975-8887
%V RTIPPR
%N 2
%P 104-109
%D 2010
%I International Journal of Computer Applications
Abstract

Ultrasonography is said to be the safest technique in medical imaging and is hence used extensively. But the images are noisy with speckle, acoustic noise and other artifacts. The classical segmentation methods fail completely or require post processing step to remove invalid object boundaries in the segmentation results. Problems associated with traditional mode, initialization and poor convergence to concave boundaries of the snakes, however, have limited their utility. A new external force for active contours largely solves both problems. This external force, call gradient vector flow (GVF), is computed as a diffusion of the gradient vectors of a gray-level or binary edge map derived from the image. The resultant field has a large capture range and forces active contours into concave regions. The intensity images are input to the method and a GVF snake is initialized. The snake deforms and finally reveals the contour of the kidney. The proposed method has successfully segmented the kidney part from the ultrasound images.

References
  1. B. V. Dhandra, Ravindra Hegadi, Mallikarjun Hangarge, V. S. Malemath: Endoscopic image classification based on active contours without edges. ICDIM 2006: 167-172
  2. Bakker, J.,Olree, M., Kaatee, R., de Lange, E.E. and Beek, R.J.A., (1997): ‘Invitro Measurement of Kidney Size: Comparison of Ultrasonography and MRI’, Ultrasound Med. Biol. 24, pp. 683 – 688.
  3. Matre, K., Stokke, E.M., Martens, D. and Gilja, O.H., (1999): ‘Invitro Volume Estimation of Kidneys using 3-D Ultrasonography and a Position Sensor’, Eur. J. Ultrasound, 10, pp. 65 – 73.
  4. Jun Xie, Yifeng Jiang and Hung-tat Tsui, (2005): ‘Segmentation of Kidney from Ultrasound Images Based on texture and Shape Priors’, IEEE Trans. on Medical Imaging, 24 , pp. 45 – 57.
  5. Marcos Martin-Fernandez and Carlos Alberola-Lopez, (2005): ‘An Approach for Contour Detection of Human Kidney from Ultrasound Images using Markov Random Fields and Active Contours’, Medical Image Analysis, 9, pp. 1 – 23.
  6. Abouzar Eslami, Shohreh Kasaei and Mehran Jahed, (2004): ‘Radial Multiscale Cyst Segmentation in Ultrasound Images of Kidney’, Proc.4th IEEE International Symposium on Signal Processing and Information Technology, Rome, Italy, pp. 42– 45.
  7. M. Kass, A. Witkin, D. Terzopoulos, “Snakes: Active Contour Models”, International Journal of Computer Vision, Vol. 1(4), pp.321-331, 1988.
  8. C. Xu and J. L. Prince, ``Snakes, Shapes, and Gradient Vector Flow,'' IEEE Transactions on Image Processing, 7(3), pp. 359-369, March 1998.
  9. Nikos Paragois, Olivier Mellina, V.Ramesh, Gradient Vector Flow Fast Geometric Active Contours, IEEE transactions on and Machine Intelligence Volume 26 , Issue 3 (March 2004) : 402 - 407
  10. K Bommanna Raja, M. Madheswaran, K. Thyagarajah, “A General Segmentation scheme for contouring Kidney Region in Ultrasound Kidney Images using improved Higher order Spline Interpolation”, 81-88, International Journal of Biological, Biomedical and Medical Sciences, Spring.2007
  11. Tim McInerney, Demetri Terzopoulos, Dept of Computer Science, University of Toronto, Toronto, ON, Canada, “Deformable Models in Medical Image Analysis: A survey”, Medical Image Analysis, l (2):91-108, 1996.
  12. C. Xu, A. Yezzi, Jr., and J. L. Prince, "A Summary of Geometric Level-Set Analogues for a General Class of Parametric Active Contour and Surface Models", in Proc. of 2001 IEEE Workshop on Variational and Level Set Methods in Computer Vision (VLSM 2001), pp. 104-111, July 2001.
  13. C. Xu, D. L. Pham, and J. L. Prince, "Medical Image Segmentation Using Deformable Models," SPIE Handbook on Medical Imaging - Volume III: Medical Image Analysis, edited by J.M. Fitzpatrick and M. Sonka, May 2000
  14. C. Xu, D. L. Pham, and J. L. Prince, "Image Segmentation Using Deformable Models", in Handbook of Medical Imaging: Volume 2. Medical Image Processing and Analysis, eds. M. Sonka and J. M. Fitzpatrick, SPIE Press, pp.129-174, 2000.
  15. L. D. Cohen. “On active contour models and balloons”. ComputerVision, Graphics, and Image Processing, 53(2):211–218, March1991.
  16. L. D. Cohen and I. Cohen, “Finite-element methods for active contour models and balloons for 2-D and 3-D images,” IEEE Trans. Pattern Anal. Machine Intell., vol. 15, pp. 1131–1147, Nov. 1993
Index Terms

Computer Science
Information Sciences

Keywords

Deformable models medical image segmentation active contours level sets GVF