Comparison of Edge Detection Algorithms for Automated Radiographic Measurement of the Carrying Angle

Authors

  • Mason AlNouri Hamad Medical Corporation, Department of Orthopedic Surgery, Doha, Qatar
  • Jasim Al Saei Hamad Medical Corporation, Department of Orthopedic Surgery, Doha, Qatar
  • Manaf Younis Hamad Medical Corporation, Department of Orthopedic Surgery, Doha, Qatar
  • Fadi Bouri Hamad Medical Corporation, Department of Orthopedic Surgery, Doha, Qatar
  • Mohamed Ali Al Habash Hamad Medical Corporation, Department of Orthopedic Surgery, Doha, Qatar
  • Mohammed Hamza Shah Hamad Medical Corporation, Department of Radiology, Doha, Qatar
  • Mohammed Al Dosari Hamad Medical Corporation, Bone and Joint Center, Doha, Qatar

DOI:

https://doi.org/10.14738/jbemi.26.1753

Keywords:

edge detection, carrying angle, elbow, automation, radiographic

Abstract

Many geometrical angles are measured directly on bone radiographs and are difficult to recall, we wanted to explore an automatic method of measurement. Edge detection was needed to determine bone edges and use them for calculation. There is no consensus on which is the best one for use in skeletal radiographs. We decided to compare commonly used edge detection methods qualitatively and quantitatively for measuring the carrying angle of the elbow using a framework we developed in PHP: Hypertext Preprocessor. Five-Hundred patients’ elbow radiographs were collected. They were run through the measurement algorithm using the following edge detection methods: Sobel, Scharr, Prewitt, Frei-Chen, Kirsch, Robinson, Difference of Gaussians (DoG), Laplacian of Gaussian (LoG), Canny, Hough. Five observers manually measured the carrying angle. Results were compared using Intraclass Correlation Coefficient (ICC), Regression Analysis and Validity calculation. The Robinson algorithm was best in the qualitative analysis. Observer ICC was 0.643 which showed a strong agreement. Quantitative analysis revealed that, developing bone caused a significant bias compared to mature bone and DoG algorithm was the best due to low bias, high validity and low processing time. Automated radiographic measurement of the carrying angle of the elbow is a feasible and reliable process.

References

(1) Pope, T.L., et al., Imaging of the Musculoskeletal System. 2nd Edition ed. Expert Radiology Series2008: Saunders. 2336.

(2) Hak, D.J. and T.L. Gautsch, A review of radiographic lines and angles used in orthopedics. Am J Orthop (Belle Mead NJ), 1995. 24(8): p. 590-601.

(3) Davies, E.R., Chapter 5 - Edge Detection, in Computer and Machine Vision (Fourth Edition), E.R. Davies, Editor 2012, Academic Press: Boston. p. 111-148.

(4) Grafova, L., et al., Study of edge detection task in dental panoramic radiographs. Dentomaxillofac Radiol, 2013. 42(7): p. 20120391.

(5) Tariq, H. and S.M.A. Burney, Contour Extraction of Femur and Tibia Condyles on Plain Anteroposterior (AP) Radiograph. International Journal of Computer Applications, 2012. 52(15): p. 26-30.

(6) Aydin, A., T. Ibrikci, and I.D. Akcali, A hybrid image processing system for X-ray images of an external fixator. Comput Methods Biomech Biomed Engin, 2012. 15(7): p. 753-9.

(7) Zhang, J., et al., Automatic Cobb measurement of scoliosis based on fuzzy Hough Transform with vertebral shape prior. J Digit Imaging, 2009. 22(5): p. 463-72.

(8) Conrozier, T., et al., Reproducibility and sensitivity to change of a new method of computer measurement of joint space width in hip osteoarthritis. Performance of three radiographic views obtained at a 3-

year interval. Osteoarthritis Cartilage, 2009. 17(7): p. 864-70.

(9) Qian, W., et al., Tree-structured nonlinear filters in digital mammography. IEEE Trans Med Imaging, 1994. 13(1): p. 25-36.

(10) Leung, C.K., et al., Novel approach for anterior chamber angle analysis: anterior chamber angle detection with edge measurement and identification algorithm (ACADEMIA). Arch Ophthalmol, 2006. 124(10): p. 1395-401.

(11) Zhang, L.P., B.Y. Yang, and C.H. Wang, [The analysis and comparison of different edge detection algorithms in ultrasound B-scan images]. Zhongguo Yi Liao Qi Xie Za Zhi, 2006. 30(3): p. 170-2.

(12) 1Lloret, R.L., et al., Classification of left ventricular thrombi by their history of systemic embolization using pattern recognition of two-dimensional echocardiograms. Am Heart J, 1985. 110(4): p. 761-5.

(13) Pauwels, R., et al., Automated implant segmentation in cone-beam CT using edge detection and particle counting. Int J Comput Assist Radiol Surg, 2014. 9(4): p. 733-43.

(14) Shi, J., et al., Treatment response assessment of breast masses on dynamic contrast-enhanced magnetic resonance scans using fuzzy c-means clustering and level set segmentation. Med Phys, 2009. 36(11): p. 5052-63.

(15) Placidi, G., M. Alecci, and A. Sotgiu, Post-processing noise removal algorithm for magnetic resonance imaging based on edge detection and wavelet analysis. Phys Med Biol, 2003. 48(13): p. 1987-95.

(16) Lin, X., et al., Spinal cord atrophy and disability in multiple sclerosis over four years: application of a reproducible automated technique in monitoring disease progression in a cohort of the interferon beta-1a (Rebif) treatment trial. J Neurol Neurosurg Psychiatry, 2003. 74(8): p.

-4.

(17) Woodhead, H.J., et al., Measurement of midfemoral shaft geometry: repeatability and accuracy using magnetic resonance imaging and dual-energy X-ray absorptiometry. J Bone Miner Res, 2001. 16(12): p. 2251-9.

(18) Garcia, E.V., et al., Totally automatic definition of renal regions of interest from 99mTc-MAG3 renograms: validation in patients with normal kidneys and in patients with suspected renal obstruction. Nucl Med Commun, 2010. 31(5): p. 366-74.

(19) Drever, L.A., et al., Comparison of three image segmentation techniques for target volume delineation in positron emission tomography. J Appl Clin Med Phys, 2007. 8(2): p. 93-109.

(20) Wang, Z., et al., Semiautomatic segmentation and quantification of calcified plaques in intracoronary optical coherence tomography images. J Biomed Opt, 2010. 15(6): p. 061711.

(21) Rogowska, J. and M.E. Brezinski, Image processing techniques for noise removal, enhancement and segmentation of cartilage OCT images. Phys Med Biol, 2002. 47(4): p. 641-55.

(22) Wang, Z., et al., A combining method for tumors detection from near-infrared breast imaging. Conf Proc IEEE Eng Med Biol Soc, 2005. 6: p. 6508-11.

(23) Sivakamasundari, J., et al., Fpga based hardware synthesis for automatic segmentation of retinal blood vessels in diabetic retinopathy images. Biomed Sci Instrum, 2014. 50: p. 156-63.

(24) Jia, X., H. Huang, and R. Wang, A novel edge detection in medical images by fusing of multi-model from different spatial structure clues. Biomed Mater Eng, 2014. 24(1): p. 1289-98.

(25) Zhu, X., R.M. Rangayyan, and A.L. Ells, Detection of the optic nerve head in fundus images of the retina using the Hough transform for circles. J Digit Imaging, 2010. 23(3): p. 332-41.

(26) Zhu, X. and R.M. Rangayyan, Detection of the optic disc in images of the retina using the Hough transform. Conf Proc IEEE Eng Med Biol Soc, 2008. 2008: p. 3546-9.

(27) Chapman, N., et al., Computer algorithms for the automated measurement of retinal arteriolar diameters. Br J Ophthalmol, 2001. 85(1): p. 74-9.

(28) Romary, D., J.F. Lerallut, and G. Fontenier, Application of image processing techniques to gamma-angiography. Comput Biomed Res, 1985. 18(5): p. 488-95.

(29) Pijet, M., et al., Fractal analysis of heart graft acute rejection microscopic images. Transplant Proc, 2014. 46(8): p. 2864-6.

(30) Irianto, J., D.A. Lee, and M.M. Knight, Quantification of chromatin condensation level by image processing. Med Eng Phys, 2014. 36(3): p. 412-7.

(31) Ahammer, H. and T.T. DeVaney, The influence of edge detection algorithms on the estimation of the fractal dimension of binary digital images. Chaos, 2004. 14(1): p. 183-8.

(32) Konstantinidis, I., A. Santamaria-Pang, and I. Kakadiaris, Frames-Based Denoising in 3D Confocal Microscopy Imaging. Conf Proc IEEE Eng Med Biol Soc, 2005. 1: p. 290-3.

(33) Chang, M.H., et al., Facial identification in very low-resolution images simulating prosthetic vision. J Neural Eng, 2012. 9(4): p. 046012.

(34) Daponte, J.S. and M.D. Fox, Enhancement of chest radiographs with gradient operators. IEEE Trans Med Imaging, 1988. 7(2): p. 109-17.

(35) Potter, H.P., The Obliquity of the Arm of the Female in Extension. The Relation of the Forearm with the Upper Arm in Flexion. J Anat Physiol, 1895. 29(Pt 4): p. 488-91.

(36) Chapleau, J., et al., Validity of goniometric elbow measurements: comparative study with a radiographic method. Clin Orthop Relat Res, 2011. 469(11): p. 3134-40.

(37) Goldfarb, C.A., et al., Elbow radiographic anatomy: measurement techniques and normative data. J Shoulder Elbow Surg, 2012. 21(9): p. 1236-46.

(38) Morrey, B.F., Morrey's The Elbow and Its Disorders. 4th Edition ed2009: Saunders. 1232.

(39) Sobel, I. and G. Feldman, A 3x3 Isotropic Gradient Operator for Image Processing, 1968.

(40) Scharr, H., Optimale Operatoren in der digitalen Bildverarbeitung, 2000, Universitätsbibliothek.

(41) Prewitt, J.M.S., Object Enhancement and Extraction, in Picture processing and Psychopictorics, B.S. Lipkin and A. Rosenfeld, Editors. 1970, Academic Press: New York.

(42) Frei, W. and C. Chung-Ching, Fast Boundary Detection: A Generalization and a New Algorithm. Computers, IEEE Transactions on, 1977. C-26(10): p. 988-998.

(43) Kirsch, R.A., Computer determination of the constituent structure of biological images. Comput Biomed Res, 1971. 4(3): p. 315-28.

(44) Robinson, G.S., Edge detection by compass gradient masks. Computer Graphics and Image Processing, 1977. 6(5): p. 492-501.

(45) Klette, R., Concise Computer Vision: An Introduction into Theory and Algorithms. Undergraduate Topics in Computer Science2014: Springer.

(46) Marr, D. and E. Hildreth, Theory of Edge Detection. Proceedings of the Royal Society of London B: Biological Sciences, 1980. 207(1167): p. 187-217.

(47) Canny, J., A Computational Approach to Edge Detection. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 1986. PAMI-8(6): p. 679-698.

(48) Duda, R.O. and P.E. Hart, Use of the Hough transformation to detect lines and curves in pictures. Commun. ACM, 1972. 15(1): p. 11-15.

(49) Paraskevas, G., et al., Study of the carrying angle of the human elbow joint in full extension: a morphometric analysis. Surg Radiol Anat, 2004. 26(1): p. 19-23.

(50) Koch, G.G., Intraclass correlation coefficient, in Encyclopedia of Statistical Sciences, S. Kotz and N.L. Johnson, Editors. 1982, John Wiley & Sons: New York. p. 213-217.

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Published

2016-01-04

How to Cite

AlNouri, M., Al Saei, J., Younis, M., Bouri, F., Al Habash, M. A., Shah, M. H., & Al Dosari, M. (2016). Comparison of Edge Detection Algorithms for Automated Radiographic Measurement of the Carrying Angle. British Journal of Healthcare and Medical Research, 2(6), 78. https://doi.org/10.14738/jbemi.26.1753