Automatic Segmentation of Impaired Joint Space Area for Osteoarthritis Knee on X-ray Image using Gabor Filter Based Morphology Process

Lilik Anifah, I Ketut Eddy Purnama, Moch Hariadi, Mauridhi Hery Purnomo

Abstract


Segmentation is the first step in osteoarthritis classification. Manual selection is time-consuming, tedious, and expensive. The system is designed to help medical doctors to determine the region of interest of visual characteristics found in knee Osteoarthritis (OA). We propose a fully automatic method without human interaction to segment Junction Space Area (JSA) for OA classification on impaired x-ray image. In this proposed system, right and left knee detection is performed using using Contrast-Limited Adaptive Histogram Equalization (CLAHE) and template macthing. The row sum graph and moment methods are used to segment the junction space area of knee. Overall we evaluated 98 kneess of patients. Experimental results demonstrate an accuracy of the system of up to 100% for detection of both left and right knee and for junction space detection an accuracy 84.38% for the right knee and 85.42% for the left. The second experiment using gabor filter with parameter α=8, θ=0, Ψ=[0 Π/2], γ=0,8 and N=8 and row sum graph give an accuracy 92.63% for the right knee and 87.37% for the left. And the average time needs to process is 65.79 second. For obvious reasons we chose the results of the fourth to segment junction area in both right and the left knee.

Keywords


knee osteoarthritis; segmentation; joint space width; CLAHE; gabor filter

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References


http://www.wrongdiagnosis.com/o/osteoarthritis/stats-country.html

A. D. Woolf and B. Pfl g r “Burd n of major musculosk l tal conditions”, Bull World Health Organ, vol. 81, pp. 646-656, 2003.

M. J. Elders, “The increasing impact of arthritis on public health,” Journal Rheumatol, vol. 60, pp. 6-8, 2000.

J. Martel-Pelletier, D. Lajeunesse, H. Fahmi, G. Tardif, and J. P. Pelletier ,“New thoughts on the pathophysiology of osteoarthritis: one more step toward new therapeutic targets,” Curr Rheumatol Rep, vol. 8, pp. 30-6, 2006.

Buckland-Wright, “Current status of imaging procedures in the diagnosis, prognosis and monitoring of osteoarthritis,” Bailliere's Clinical Rheumatology, vol. 11, no. 4, Nov. 1997.

El Miedany, “Altered bone mineral metabolism in patients with osteoarthritis, Éditions sci ntifiqu s t médical s Els vi r SAS, 67: 521-7, 2000.

P. Podsiadlo, M. Wolski, and G. W. Stacho iak, “Automated selection of trabicular bon regions in kn radiographs”, Medical Physics, vol. 35, no. 5, pp. 1870-1882, May 2008.

L. Shamir, S. M. Ling, W. W. Scott, A. Bos, and N. Orlov, “Kn X-ray image analysis method for automated detection of Osteoarthritis,” IEEE Transactions on Biomedical Engineering, 2008, pp. 1-10.

T. L. Mengko, R. G. Wachjudi, A. B. Suksmono, and Q. Danudirdjo, “Automat d D tection of Unimpaired joint space for knee osteoarthritis assessment”, 0-7803 -8940-9/050 IEEE, pp. 400-403, 2005.

http://oai.epi-ucsf.org/datarelease/

Ost oarthritis Initiativ : A Kn H alth Study, “ adiographic Procedure Manual for Examinations of the Knee, Hand, Pelvis and Lower Limbs”, OAI, San Frasisco, 2006.

http://radonc.ucsf.edu/research_group/jpouliot/tutorial/HU/Lesson7.html

R.C. Gonzalez and R.E. Woods, Digital image processing, 1992.

D. Cavouras, “An Efficient Clahe-Based, Spot-Adaptive, Image Segmentation Technique for Improving Microarray Genes’ Quantification,” presented at 2nd International Conference on Experiments /Process/System Modelling/Simulation & Optimization 2nd IC-EpsMsO Athens, 4-7 July, 2007.

J. E. Barn s “Charact ristics and control of contrast in CT. RadioGraphics” vol. 12. 1992, pp. 825-837.

R. C. Gonzalez, R. E. Woods, and S. L. Eddins, Digital image processing using matlab, Pearson Education, 2005.

Mori, Optical Character Recognition, John Willey and Son, Canada, 1999.

M. Mehta and R. Sanchati, and A. Marchya, “Automatic Cheque Processing System,” International Journal of Computer and Electrical Engineering, vol. 2, no. 4, pp. 761-765, August 2010.

J. . Mov llan, “Tutorial on Gabor Filt rs”, 2008.

Nixon, Feature Extraction and Image Processing 2nd ed, Elsevier, 2008.

V. P. Vishwakarma, S. Pandey, and M. N. Gupta, “Adaptive Histogram Equalization and Logarithm Transform with Rescaled Low Frequency DCT Coefficients for Illumination Normalization,” International Journal of Recent Trends in Engineering, vol 1, no. 1, pp. 318-322, May 2009.




DOI: http://dx.doi.org/10.12962/j20882033.v22i3.72

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