An analysis algorithm for accurate determination of articular cartilage thickness of hip joint from MR images

Abstract

Purpose: To test the accuracy of the most widely used technique based on edge detection for thickness measurement of the hip joint cartilage in MR images, and to improve the measurement accuracy by developing a new measurement method based on a model of the MRI process.Materials and Methods: MRI was performed in 3 normal cadaver hips, 25 hips of normal volunteers, and 25 hips of patients with osteoarthritis. In general, thickness was defined as the distance between the two sides of the hip cartilage along the normal directions of the cartilage surfaces. In this article this is referred to as the ''edge detection method.'' A theoretical simulation analysis revealed that the edge detection method considerably underestimated the cartilage thickness of the hip joint. A new measurement method based on a model of the MR imaging process was accordingly proposed for correcting the measurement errors.Results: In the experiment using the cadaver hips, anatomical measurement of cartilage thickness was used as reference standard. For measurements at 35 sites, the proposed model-based method gave results similar to those presented from anatomic section, while the edge detection method gave underestimation compared with the anatomic thickness. The underestimation biases for the edge detection method were consistent with the biases predicted by theoretical simulation. In the experiment using the hips of volunteers and patients, the edge detection result was an underestimation compared with the result generated by using the model-based method. Conclusion:The edge detection method underestimated the hip cartilage thickness in MR images. The proposed model-based method was more accurate than the edge detection method for thickness measurement of the hip cartilage.

Automatic bone segmentation and bone-cartilage interface extraction for the shoulder joint from magnetic resonance images

“…A major focus of research for automatic cartilage segmentation has centred on MR imaging given its' capacity to visualize cartilage morphology, to provide measures on parameters such as volume, thickness, surface area and curvature and investigate the biochemical composition of cartilage (Link et al 2007). Currently, the majority of the research centres on the knee (Folkesson et al 2007, Brem et al 2009, Yin et al 2010, Swamy and Holi 2012, Tamez-Pena et al 2012, Marques et al 2013 and hip joints (Cheng et al 2011). Studies on shoulder cartilage morphometry have used interactive measurements (Hekimoğlu et al 2013) and manual segmentations (Massimini et al 2011).…”

Automatic detection of anatomical landmarks on the knee joint using MRI data

“…The principal reason is that the optimization is a global algorithm based on gray levels of images, and gray levels in medical images are different due to various contrasts. Other algorithms solve specific landmark detection problems by carefully studying the appearance characteristics of shape priors of the landmarks. Although these methods display robustness to common image distortions and have a low computational complexity, they demonstrate a lack of potential to be extended to other studies using different landmarks, different joints or different MR scans .…”

A Novel Method for Boundary Detection and Thickness Measurement of Two Adjacent Thin Structures from 3-D MR Images

“…In our previous study [16], a detailed model-based procedure for accurate thickness measurement of two adjacent thin structures in 2-D image plane has been described. Similar to the procedure presented in [16], we can predict the shape of the MR signal intensity profile along the normal direction given in Eq.…”

“…Similar to the procedure presented in [16], we can predict the shape of the MR signal intensity profile along the normal direction given in Eq. (19).…”

“…Based on thin structure parameters τ f c , w, τ ac , ϕ, θ, L s , L b+ and L 0 , MR imaging parameters Δ x , Δ y and Δ z , and Gaussian SD, σ, f (s) and f (s) were obtained according to Eqs. (16) and (17), respectively. Using f (s) and f (s), thickness was estimated using Eq.…”