Adaptive Segmentation of Knee Radiographs for Selecting the Optimal ROI in Texture Analysis

The purposes of this study were to investigate: 1) the effect of placement of region-of-interest (ROI) for texture analysis of subchondral bone in knee radiographs, and 2) the ability of several texture descriptors to distinguish between the knees with and without radiographic osteoarthritis (OA). Bilateral posterior-anterior knee radiographs were analyzed from the baseline of OAI and MOST datasets. A fully automatic method to locate the most informative region from subchondral bone using adaptive segmentation was developed. We used an oversegmentation strategy for partitioning knee images into the compact regions that follow natural texture boundaries. LBP, Fractal Dimension (FD), Haralick features, Shannon entropy, and HOG methods were computed within the standard ROI and within the proposed adaptive ROIs. Subsequently, we built logistic regression models to identify and compare the performances of each texture descriptor and each ROI placement method using 5-fold cross validation setting. Importantly, we also investigated the generalizability of our approach by training the models on OAI and testing them on MOST dataset.We used area under the receiver operating characteristic (ROC) curve (AUC) and average precision (AP) obtained from the precision-recall (PR) curve to compare the results. We found that the adaptive ROI improves the classification performance (OA vs. non-OA) over the commonly used standard ROI (up to 9% percent increase in AUC). We also observed that, from all texture parameters, LBP yielded the best performance in all settings with the best AUC of 0.840 [0.825, 0.852] and associated AP of 0.804 [0.786, 0.820]. Compared to the current state-of-the-art approaches, our results suggest that the proposed adaptive ROI approach in texture analysis of subchondral bone can increase the diagnostic performance for detecting the presence of radiographic OA

Identification of Two-Mass Mechanical Systems Using Torque Excitation: Design and Experimental Evaluation

This paper deals with methods for parameter estimation of two-mass mechanical systems in electric drives. Estimates of mechanical parameters are needed in the start-up of a drive for automatic tuning of model-based speed and position controllers. A discrete-time output error (OE) model is applied to parameter estimation. The resulting pulse-transfer function is transformed into a continuous-time transfer function, and parameters of the two-mass system model are analytically solved from the coefficients of this transfer function. An open-loop identification setup and two closed-speed-loop identification setups (direct and indirect) are designed and experimentally compared. The experiments are carried out at nonzero speed to reduce the effect of nonlinear friction phenomena on the parameter estimates. According to results, all three identification setups are applicable for the parameter estimation of two-mass mechanical systems.Peer reviewe

Multimodal Machine Learning-based Knee Osteoarthritis Progression Prediction from Plain Radiographs and Clinical Data

Knee osteoarthritis (OA) is the most common musculoskeletal disease without a cure, and current treatment options are limited to symptomatic relief. Prediction of OA progression is a very challenging and timely issue, and it could, if resolved, accelerate the disease modifying drug development and ultimately help to prevent millions of total joint replacement surgeries performed annually. Here, we present a multi-modal machine learning-based OA progression prediction model that utilizes raw radiographic data, clinical examination results and previous medical history of the patient. We validated this approach on an independent test set of 3,918 knee images from 2,129 subjects. Our method yielded area under the ROC curve (AUC) of 0.79 (0.78-0.81) and Average Precision (AP) of 0.68 (0.66-0.70). In contrast, a reference approach, based on logistic regression, yielded AUC of 0.75 (0.74-0.77) and AP of 0.62 (0.60-0.64). The proposed method could significantly improve the subject selection process for OA drug-development trials and help the development of personalized therapeutic plans

Discrete-Time Observer Design for Sensorless Synchronous Motor Drives

This paper deals with the speed and position estimation of interior permanent-magnet synchronous motor (IPM) and synchronous reluctance motor (SyRM) drives. A speed-adaptive full-order observer is designed and analyzed in the discrete-time domain. The observer design is based on the exact discrete-time motor model, which inherently takes the delays in the control system into account. The proposed observer is experimentally evaluated using a 6.7-kW SyRM drive. The analysis and experimental results indicate that major performance improvements can be obtained with the direct discrete-time design, especially if the sampling frequency is relatively low compared to the fundamental frequency. The ratio below 10 between the sampling and fundamental frequencies is achieved in experiments with the proposed discrete-time design.Peer reviewe

Effects of optical beam angle on quantitative optical coherence tomography (OCT) in normal and surface degenerated bovine articular cartilage

2010-2011 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

Micro-Scale Distribution of CA4+ in Ex vivo Human Articular Cartilage Detected with Contrast-Enhanced Micro-Computed Tomography Imaging

Contrast-enhanced micro-computed tomography (CE mu CT) with cationic and anionic contrast agents reveals glycosaminoglycan (GAG) content and distribution in articular cartilage (AC). The advantage of using cationic stains (e.g., CA4+) compared to anionic stains (e.g., Hexabrix (R)), is that it distributes proportionally with GAGs, while anionic stain distribution in AC is inversely proportional to the GAG content. To date, studies using cationic stains have been conducted with sufficient resolution to study its distributions on the macro-scale, but with insufficient resolution to study its distributions on the micro-scale. Therefore, it is not known whether the cationic contrast agents accumulate in extra/pericellular matrix and if they interact with chondrocytes. The insufficient resolution has also prevented to answer the question whether CA4+ accumulation in chondrons could lead to an erroneous quantification of GAG distribution with low-resolution mu CT setups. In this study, we use high-resolution mu CT to investigate whether CA4+ accumulates in chondrocytes, and further, to determine whether it affects the low-resolution ex vivo mu CT studies of CA4+ stained human AC with varying degree of osteoarthritis. Human osteochondral samples were immersed in three different concentrations of CA4+ (3 mgI/ml, 6 mgI/ml, and 24 mgI/ml) and imaged with high-resolution mu CT at several timepoints. Different uptake diffusion profiles of CA4+ were observed between the segmented chondrons and the rest of the tissue. While the X-ray -detected CA4+ concentration in chondrons was greater than in the rest of the AC, its contribution to the uptake into the whole tissue was negligible and in line with macro-scale GAG content detected from histology. The efficient uptake of CA4+ into chondrons and surrounding territorial matrix can be explained by the micro-scale distribution of GAG content. CA4+ uptake in chondrons occurred regardless of the progression stage of osteoarthritis in the samples and the relative difference between the interterritorial matrix and segmented chondron area was less than 4%. To conclude, our results suggest that GAG quantification with CE mu CT is not affected by the chondron uptake of CA4+. This further confirms the use of CA4+ for macro-scale assessment of GAG throughout the AC, and highlight the capability of studying chondron properties in 3D at the micro scale.Peer reviewe

Laser-ultrasonic delivery of agents into articular cartilage

Peer reviewe