Quantitative assessment technique based on magnetic resonance image processing for determining the incidence of articular cartilage morbidity

• Main Author: Krishnaswamy, Abhirami.
• Other Authors: Poh Chueh Loo
• Format: Final Year Project
• Language: English
• Published: 2010
• Subjects: DRNTU::Engineering::Bioengineering
• Online Access: http://hdl.handle.net/10356/39734
• Institution: Nanyang Technological University

BACKGROUND & MOTIVATION Knee osteoarthritis (OA) is caused by the progressive loss of articular cartilage and the parallel formation of osteocytes leading to tenacious affliction and functional restrictions in the affected joints. Pronouncement of the disease using MR imaging has become prevalent, where the damage, which is seen as brighter regions within the cartilage volume, may be visually diagnosticated by a trained radiologist. As a result, there is a need to better enable clinicians in performing an accurate analysis for their patients to determine if their cartilage has been damaged significantly, in particular, through quantitative metrology. OBJECTIVES & METHODS A fifteen stage methodology schematic, involving manual segmentation of MR image sets, image contrast enhancement, pixel intensity extraction, Gaussian curve-fitting, statistical data analysis, damage volume characterization, benchmark-threshold estimation and 3-D visualization, was derived. This schematic was then utilized to quantitatively determine if there is significant difference in pixel intensities between the healthy and damaged cartilage segments of MR data sets containing known incidence of OA. RESULTS & CONCLUSION Results were conclusive, establishing (1) significant differences in pixel intensity between healthy and damaged cartilage (p-value= 0.006), (2) no significant differences in pixel intensity between healthy segments of damaged cartilage and the control samples (power of test=0.984), (3) a linear correlation (correlation coefficient of 0.894; R2=0.7985) between the Mean-Median and CD% values of the test samples and (4) significant visualization at the 95% confidence interval (Average T=135; Average PD%=84%). Implications of these results have been discussed and the considerations for future work have been extensively put forth. Overall, the results obtained are indicative of a strong potential for future research in the area of quantitative medicine with respect to signal intensity variations in Osteoarthritic cartilages of the knee.