Visualization of Cartilage from Knee Joint Magnetic Resonance Images and Quantitative Assessment to Study the Effect of Age, Gender and Body Mass Index (BMI) in Progressive Osteoarthritis (OA)

Author(s): Mallikarjunaswamy Shivagangadharaiah Matada* , Mallikarjun Sayabanna Holi , Rajesh Raman , Sujana Theja Jayaramu Suvarna .

Journal Name: Current Medical Imaging

Volume 15 , Issue 6 , 2019

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Graphical Abstract:


Background: Osteoarthritis (OA) is a degenerative disease of joint cartilage affecting the elderly people around the world. Visualization and quantification of cartilage is very much essential for the assessment of OA and rehabilitation of the affected people. Magnetic Resonance Imaging (MRI) is the most widely used imaging modality in the treatment of knee joint diseases. But there are many challenges in proper visualization and quantification of articular cartilage using MRI. Volume rendering and 3D visualization can provide an overview of anatomy and disease condition of knee joint. In this work, cartilage is segmented from knee joint MRI, visualized in 3D using Volume of Interest (VOI) approach.

Methods: Visualization of cartilage helps in the assessment of cartilage degradation in diseased knee joints. Cartilage thickness and volume were quantified using image processing techniques in OA affected knee joints. Statistical analysis is carried out on processed data set consisting of 110 of knee joints which include male (56) and female (54) of normal (22) and different stages of OA (88). The differences in thickness and volume of cartilage were observed in cartilage in groups based on age, gender and BMI in normal and progressive OA knee joints.

Results: The results show that size and volume of cartilage are found to be significantly low in OA as compared to normal knee joints. The cartilage thickness and volume is significantly low for people with age 50 years and above and Body Mass Index (BMI) equal and greater than 25. Cartilage volume correlates with the progression of the disease and can be used for the evaluation of the response to therapies.

Conclusion: The developed methods can be used as helping tool in the assessment of cartilage degradation in OA affected knee joint patients and treatment planning.

Keywords: Knee joint diseases, magnetic resonance imaging, osteoarthritis, 3D visualization, body mass index, osteoarthritis.

Levangie K, Cynthia CN. Joint structure and function: A comprehensive analysis. FA Davis Company 2006.
Barbour KE, Helmick CG, Boring M, Brady TJ. Vital signs: Prevalence of doctor-diagnosed arthritis and arthritis-attributable activity limitation-United States 2013-2015. MMWR Morb Mortal Wkly Rep 2017; 66(9): 246-53.
Pal CP, Singh P, Chaturvedi S, Pruthi KK, Vij A. Epidemiology of knee osteoarthritis in India and related factors. Indian J Orthop 2016; 50(5): 518-22.
Sharma MK, Swami HM, Bhatia V, Verma A, Bhatia S, Kaur G. An epidemiological study of correlates of osteo-arthritis in geriatric population of UT Chandigarh. Indian J Community Med 2007; 32: 77-8.
Kellgren JH, Lawrence JS. Radiological assessment of osteo-arthrosis. Ann Rheum Dis 1957; 16(4): 494-502.
Hayashi D, Roemer FW, Jarraya M, Guermazi A. Imaging of Osteoarthritis Geriatric Imaging. Berlin, Heidelberg: Springer 2013.
Anastasi G, Bramanti P, Di Bella P, et al. Volume rendering based on magnetic resonance imaging: Advances in understanding the three-dimensional anatomy of the human knee. J Anat 2007; 211(3): 399-406.
Jayaram K. Udupa, Gabor T Herman 3D Imaging in medicine. 2nd ed. CRC Press 1999.
Cashman PM, Kitney RI, Gariba MA, Carter ME. Automated techniques for visualization and mapping of articular cartilage in MR images of the osteoarthritic knee: A base technique for the assessment of microdamage and submicro damage. IEEE Trans Nanobioscience 2002; 1(1): 42-51.
Poh CL, Kitney R. Viewing interfaces for segmentation and measurement results. Conf Proc IEEE Eng Med Biol Soc 2005; 5: 5132-5.
Meibner M, Pfister H, Westerman R, Wittenbrink CM. Volume visualization and volume rendering techniques. The Eurographics Association 2000.
Cicuttini F, Forbes A, Morris K, Darling S, Bailey M, Stuckey S. Gender differences in knee cartilage volume as measured by magnetic resonance imaging. Osteoarthritis Cartilage 1999; 7(3): 265-71.
Ding C, Cicuttini F, Scott F, Glisson M, Jones G. Sex differences in knee cartilage volume in adults: Role of body and bone size, age and physical activity. Rheumatology (Oxford) 2003; 42(11): 1317-23.
Sanghi D, Srivastava RN, Singh A, Kumari R, Mishra R, Mishra A. The assciation of anthropometric measures and osteoarthritis knee in non-obese subjects: A cross sectional study. Clinics (São Paulo) 2011; 66(2): 275-9.
Swamy MSM, Holi MS. Segmentation, visualization and quantification of knee joint articular cartilage using MR images. In: Swamy P, Guru D, Eds. Multimedia Processing. Communication and Computing Applications Lecture Notes in Electrical Engineering 2013.
Mallikarjunaswamy MS, Holi MS, Raman R. Quantification and 3D visualization of articular cartilage of knee joint using image processing techniques. In: Jain L, Behera H, Mandal J, Mohapatra D, Eds. Computational Intelligence in Data Mining Smart Innovation, Systems and Technologies. Springer 2015.
Canny J. A computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell 1986; 8(6): 679-98.
Teichtahl AJ, Parkins K, Hanna F, et al. The relationship between the angle of the trochlear groove and patella cartilage and bone morphology-a cross-sectional study of healthy adults. Osteoarthritis Cartilage 2007; 15(10): 1158-62.
Heuer F, Sommers M, Reid JB, Bottlang M. Estimation of cartilage thickness from joint surface scans: Comparative analysis of computational methods. In: Proceedings of the American Society of Mechanical Engineers Annual Meeting. 569-70.
Kauffmann C, Gravel P, Godbout B, et al. Computer-aided method for quantification of cartilage thickness and volume changes using MRI: validation study using a synthetic model. IEEE Trans Biomed Eng 2003; 50(8): 978-88.
Lorensen WE, Cline HE. Marching cubes: A high resolution 3D surface construction algorithm. Comput Graph 1987; 21(4): 163-9.

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Article Details

Year: 2019
Page: [565 - 572]
Pages: 8
DOI: 10.2174/1573405614666181018123251
Price: $58

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