Iranian Journal of Radiology

Published by: Kowsar

Measuring the Effect of Filters on Segmentation of Developmental Dysplasia of the Hip

Hasan Erdinc Kocer 1 , Kerim Kursat Cevik 2 , * , Mesut Sivri 3 and Mustafa Koplay 3
Authors Information
1 Department of Electronics and Computer Education, Technical Education Faculty, Selcuk University, Konya, Turkey
2 Department of Computer Programming, Bor Vocational High School, Nigde University, Nigde, Turkey
3 Department of Radiology, Medical Faculty, Selcuk University, Konya, Turkey
Article information
  • Iranian Journal of Radiology: July 01, 2016, 13 (3); e25491
  • Published Online: February 17, 2016
  • Article Type: Research Article
  • Received: January 6, 2015
  • Revised: May 11, 2015
  • Accepted: May 13, 2015
  • DOI: 10.5812/iranjradiol.25491

To Cite: Kocer H E, Cevik K K, Sivri M, Koplay M. Measuring the Effect of Filters on Segmentation of Developmental Dysplasia of the Hip, Iran J Radiol. 2016 ; 13(3):e25491. doi: 10.5812/iranjradiol.25491.

Abstract
Copyright © 2016, Tehran University of Medical Sciences and Iranian Society of Radiology. This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/) which permits copy and redistribute the material just in noncommercial usages, provided the original work is properly cited.
1. Background
2. Objectives
3. Materials and Methods
4. Results
5. Discussion
Acknowledgements
Footnotes
References
  • 1. Atalar H, Yavuz OY, Uras I, Gunay, C. , Saylı, U. . Frequency of Evaluation of Developmental Hip Dysplasia Screening Program in Turkey. Turkey Clinics J Med Sci. 2008; 28: 357-60
  • 2. Tosun HB, Bulut M, Karakurt L, Belhan O, Serbest S. Evaluation of the Results of Hip Ultrasonography which Applied for Screening of Developmental Hip Dysplasia. Fırat Med J. 2010; 15(4): 178-83
  • 3. Song KM, Lapinsky A. Determination of hip position in the Pavlik harness. J Pediatr Orthop. 2000; 20(3): 317-9[PubMed]
  • 4. Rosendahl K, Markestad T, Lie RT. Ultrasound screening for developmental dysplasia of the hip in the neonate: the effect on treatment rate and prevalence of late cases. Pediatrics. 1994; 94(1): 47-52[PubMed]
  • 5. Herring JA. Tachdjian's Pediatric Orthopaedics. 2003; : 513-34
  • 6. Wan MH, Supriyanto E. Comparative evaluation of ultrasound kidney image enhancement techniques. Int J Comput Appl. 2011; 21(7): 15-9
  • 7. Hip joint segmentation from 2D ultrasound data based on dynamic shape priors. Proceedings of the 4th WSEAS international conference on Electronics, control and signal processing. : 245-50
  • 8. Parametric 3D Hip Joint Segmentation for the Diagnosis of Developmental Dysplasia. Engineering in Medicine and Biology Society, 2006. EMBS '06. 28th Annual International Conference of the IEEE. : 4807-10
  • 9. Application to the Diagnosis of Developmental Dysplasia of the Hip. Analysis of Ultrasound Images Based on Local Statistics. : 2531-4
  • 10. Application of Segmentation and Measurement in the Treatment of Developmental Dysplasia of the Hip. Bioinformatics and Biomedical Engineering, 2007. ICBBE 2007. The 1st International Conference on. : 989-91
  • 11. Chan TF, Vese LA. Active contours without edges. IEEE Trans Image Process. 2001; 10(2): 266-77[DOI][PubMed]
  • 12. Sapiro G. Geometric partial differential equations and image analysis. 2006; [DOI]
  • 13. Longest PW, Tian G. Transient absorption of inhaled vapors into a multilayer mucus–tissue–blood system. Anns of biomed eng. 2010; 38(2): 517-36
  • 14. Xu C, Prince JL. Snakes, shapes, and gradient vector flow. IEEE Trans Image Process. 1998; 7(3): 359-69[DOI][PubMed]
  • 15. Chan TF, Sandberg BY, Vese LA. Active Contours without Edges for Vector-Valued Images. J Vis Commun Image Representation. 2000; 11(2): 130-41[DOI]
  • 16. Chesnaud C, Refregier P, Boulet V. Statistical region snake-based segmentation adapted to different physical noise models. IEEE Trans Pattern Anal Mach Intell. 1999; 21(11): 1145-57[DOI]
  • 17. Level set evolution without re-initialization: a new variational formulation. Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on. : 430-6
  • 18. Suri JS, Liu K, Singh S, Laxminarayan SN, Zeng X, Reden L. Shape recovery algorithms using level sets in 2-D/3-D medical imagery: a state-of-the-art review. IEEE Trans Inf Technol Biomed. 2002; 6(1): 8-28[PubMed]
  • 19. Osher S, Sethian JA. Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations. Journal of Computational Physics. 1988; 79(1): 12-49[DOI]
  • 20. Mass segmantation on mammograms using active contours. Signal Processing and Communications Applications Conference (SIU), 2013 21st. : 1-4
  • 21. Narayanan SK, Wahidabanu RSD. A view on despeckling in ultrasound imaging. Int J Signal Processing. 2009; 2(3): 85-98
  • 22. Loupas T, McDicken WN, Allan PL. An adaptive weighted median filter for speckle suppression in medical ultrasonic images. Circuits and Sys, IEEE Trans on. 1989; 36(1): 129-35
  • 23. Mahmood NH, Razif MR, Gany MT. Comparison between median, unsharp and wiener filter and its effect on ultrasound stomach tissue image segmentation for pyloric stenosis. Inte J Appl Sci Technol. 2011; 1(5)
  • 24. Sarode MV, Deshmukh PR. Reduction of speckle noise and image enhancement of images using filtering technique. Int J AdvancTechnol. 2011; 2(1): 30-8
  • 25. Thangavel K, Manavalan R, Aroquiaraj IL. Removal of speckle noise from ultrasound medical image based on special filters: comparative study. ICGST-GVIP J. 2009; 9(3): 25-32
  • 26. Young IT, van Vliet LJ. Recursive implementation of the Gaussian filter. Signal Processing. 1995; 44(2): 139-51[DOI]
Creative Commons License Except where otherwise noted, this work is licensed under Creative Commons Attribution Non Commercial 4.0 International License .

Search Relations:

Author(s):

Article(s):

Create Citiation Alert
via Google Reader

Readers' Comments