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

Assessing the physiological effects of an exercise intervention in older adults: Is there a role for core-stability training?

Preliminary evidence indicates that age-related changes in trunk muscle morphology and function are associated with decreased balance and increased falls risk. However, the associations between trunk muscle morphology, strength, and functional ability, as well as the trainability of these muscles are not well established. Therefore, the aims of this thesis were to identify the relationships between trunk muscle morphology, strength, and functional ability and to determine the effects of exercise training on these outcomes in healthy older adults. We initially undertook a systematic review to determine the effect of exercise training on trunk muscle morphology. Our results identified motor control and machine-based exercises targeting the trunk muscles resulted in the largest change in the trunk muscle morphology. Using a cross-sectional design, we then explored the relationships between trunk muscle morphology, strength, and functional ability in 64 older adults. Our results showed anterior and lateral abdominal and posterior trunk muscle size and strength were positively associated with functional ability. Finally, we conducted a randomised clinical trial investigating the effectiveness of a 12-week exercise programme on trunk muscle size, strength, and functional ability. Sixty-four individuals (mean(SD) age 69.8 (7.5) years; 59.4% female) were randomised to receive a multimodal exercise program comprising walking and balance exercises with or without strength/motor control training of the trunk muscles. Participants performing the trunk strengthening exercises experienced larger increases (mean difference [95%CI]) in trunk muscle hypertrophy (1.6 [1.0, 2.2] cm) and composite trunk strength (172.6 [100.8, 244.5] N), as well as 30-Second Chair Stand Test (5.9 [3.3, 8.4] repetitions), Sitting and Rising Test (1.2 [0.22, 2.2] points), Forward Reach Test (4.2 [1.8, 6.6] cm), Backward Reach Test (2.4 [0.22, 4.5] cm), and Timed Up and Go Test (-0.74 [-1.4, -0.03] seconds) outcomes. These findings further our understanding regarding 1) the relationships between trunk muscle morphology, strength, and functional ability and 2) appropriate exercise prescription aimed at improving these outcomes in older individuals

Sparse MDMO: learning a discriminative feature for micro-expression recognition

Micro-expressions are the rapid movements of facial muscles that can be used to reveal concealed emotions. Recognizing them from video clips has a wide range of applications and receives increasing attention recently. Among existing methods, the main directional mean optical-flow (MDMO) feature achieves state-of-the-art performance for recognizing spontaneous micro-expressions. For a video clip, the MDMO feature is computed by averaging a set of atomic features frame-by-frame. Despite its simplicity, the average operation in MDMO can easily lose the underlying manifold structure inherent in the feature space. In this paper we propose a sparse MDMO feature that learns an effective dictionary from a micro-expression video dataset. In particular, a new distance metric is proposed based on the sparsity of sample points in the MDMO feature space, which can efficiently reveal the underlying manifold structure. The proposed sparse MDMO feature is obtained by incorporating this new metric into the classic graph regularized sparse coding (GraphSC) scheme. We evaluate sparse MDMO and four representative features (LBP-TOP, STCLQP, MDMO and FDM) on three spontaneous micro-expression datasets (SMIC, CASME and CASME II). The results show that sparse MDMO outperforms these representative features