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© 2017, Springer Science+Business Media, LLC. The effects of depth and top width of transverse rectangular grooves on film thickness and friction in elastohydrodynamic lubrication (EHL) regime were investigated through numerical simulations. Results were obtained in the form of pressure profiles and Stribeck curves for central and minimum film thickness and for friction coefficient. The results indicate that grooves with narrow top widths reduce the minimum film thickness and that this reduction is greater for deeper grooves. Lubricant shearing inside these grooves was further identified as a dominant factor contributing to friction. Near the groove edges, however, no evidence of micro-EHL effect was observed. Based on the results, a groove volume parameter was proposed to characterise the groove lubrication efficiency. We found that the parameter was linearly related to the average central film thickness and by increasing the groove wavelength the film could be made thicker than that of a smooth contact.

Current assessment of osteoarthritis (OA) is primary based on visual grading of joint space narrowing and osteophytes present on radiographs. The approach is observer-dependent, not sensitive enough for the detection of the early stages of OA and time consuming. A promising solution is through fractal analysis of trabecular bone (TB) textures on radiographs. The goal is to develop an automated decision support system for the detection and prediction of OA based on TB texture regions selected on knee and hand radiographs. In this review, we describe our progress towards this development which was conducted in five stages, i.e., (i) development of automated methods for the selection of TB texture regions on knee and hand radiographs (ii), development of fractal signature methods for TB texture analysis, (iii) applications of the methods in the analysis of x-ray images of knees and hands, (iv) development of TB texture classification system, and (v) development of ReadMyXray website for knee x-ray analysis. The results achieved so far are encouraging and it is hoped, that once the system is fully developed and evaluated, it will be used to aid medical practitioners in the decision-making, i.e., in designing OA preventative measures, treatments and monitoring the OA progression.

In this paper, a new probabilistic model using measures of classifier competence and diversity is proposed. The multiple classifier system (MCS) based on the dynamic ensemble selection scheme was constructed using both developed measures. Two different optimization problems of ensemble selection are defined and a solution based on the simulated annealing algorithm is presented. The influence of minimum value of competence and diversity in the ensemble on classification performance was investigated. The effectiveness of the proposed dynamic selection methods and the influence of both measures were tested using seven databases taken from the UCI Machine Learning Repository and the StatLib statistical dataset. Two types of ensembles were used: homogeneous or heterogeneous. The results show that the use of diversity positively affects the quality of classification. In addition, cases have been identified in which the use of this measure has the greatest impact on quality.

Objective: To develop a system for predicting the progression of radiographic knee osteoarthritis (OA) using tibial trabecular bone texture. Methods: We studied 203 knees with (n = 68) or without (n = 135) radiographic tibiofemoral OA in 105 subjects (90 men and 15 women with a mean age of 54 years) in whom 2 sets of knee radiographs were obtained 4 years apart. We determined medial and lateral compartment tibial trabecular bone texture using an automated region selection method. Three texture parameters were calculated: roughness, degree of anisotropy, and direction of anisotropy based on a signature dissimilarity measure method. We evaluated tibiofemoral OA progression using a radiographic semiquantitative outcome: an increase in the medial joint space narrowing (JSN) grade. We examined the predictive ability of trabecular bone texture in knees with and those without preexisting radiographic OA, with adjustment for age, sex, and body mass index, using logistic regression (generalized estimating equations) and receiver operating characteristic curves. Results: The prediction of increased medial JSN in knees with or without preexisting radiographic OA was the most accurate for medial trabecular bone texture; the area under the curve (AUC) was 0.77 and 0.75, respectively. For lateral trabecular bone texture, the AUC was 0.71 in knees with preexisting OA and 0.72 in knees without preexisting OA. Conclusion: We have developed a system, based on analyzing tibial trabecular bone texture, which yields good prediction of loss of tibiofemoral joint space. e predictive ability of the system needs to be further validated.

There is a growing need for classification systems that can accurately detect and predict knee osteoarthritis (OA) from plain radiographs. For this purpose, a system based on a support vector machine (SVM) classifier and distances measured between trabecular bone (TB) texture images was developed and tested in previous work. Unlike other systems, it allows an image classification without the calculation and selection of numerous texture features, and it is invariant to a range of imaging conditions encountered in a routine X-ray screening of knees. Although the system exhibited 85.4% classification accuracy in OA detection, which was higher than those obtained from other systems, its performance could be further improved. To achieve this, a dissimilarity-based multiple classifier (DMC) system is developed in this study. The system measures distances between TB texture images and generates a diverse ensemble of classifiers using prototype selection, bootstrapping of training set and heterogeneous classifiers. A measure of competence is used to select accurate (i.e. better-than-random) classifiers from the ensemble, which are then combined through the majority voting rule. To evaluate the newly developed system in OA detection (prediction of OA progression), TB texture images selected on standardised radiographs of healthy and OA (non-progressive and progressive OA) knees were used. The results obtained showed that the DMC system has higher classification accuracies for the detection (90.51% with 87.65% specificity and 93.33% sensitivity) and prediction (80% with 82.00% specificity and 77.97% sensitivity) than other systems, indicating its potential as a decision-support tool for the assessment of radiographic knee OA.

In the paper measures of classifier competence and diversity using a probabilistic model are proposed. The multiple classifier system (MCS) based on dynamic ensemble selection scheme was constructed using both measures developed. The performance of proposed MCS was compared against three multiple classifier systems using six databases taken from the UCI Machine Learning Repository and the StatLib statistical dataset. The experimental results clearly show the effectiveness of the proposed dynamic selection methods regardless of the ensemble type used (homogeneous or heterogeneous). © 2011 Springer-Verlag.

PURPOSE: The purpose of this study is to develop a dissimilarity measure for the classification of trabecular bone (TB) texture in knee radiographs. Problems associated with the traditional extraction and selection of texture features and with the invariance to imaging conditions such as image size, anisotropy, noise, blur, exposure, magnification, and projection angle were addressed. METHODS: In the method developed, called a signature dissimilarity measure (SDM), a sum of earth mover's distances calculated for roughness and orientation signatures is used to quantify dissimilarities between textures. Scale-space theory was used to ensure scale and rotation invariance. The effects of image size, anisotropy, noise, and blur on the SDM developed were studied using computer generated fractal texture images. The invariance of the measure to image exposure, magnification, and projection angle was studied using x-ray images of human tibia head. For the studies, Mann-Whitney tests with significance level of 0.01 were used. A comparison study between the performances of a SDM based classification system and other two systems in the classification of Brodatz textures and the detection of knee osteoarthritis (OA) were conducted. The other systems are based on weighted neighbor distance using compound hierarchy of algorithms representing morphology (WND-CHARM) and local binary patterns (LBP).RESULTS: Results obtained indicate that the SDM developed is invariant to image exposure (2.5-30 mA s), magnification (x1.00 - x1.35), noise associated with film graininess and quantum mottle (<25%), blur generated by a sharp film screen, and image size (> 64 x 64 pixels). However, the measure is sensitive to changes in projection angle (> 5 degrees), image anisotropy (> 30 degrees), and blur generated by a regular film screen. For the classification of Brodatz textures, the SDM based system produced comparable results to the LBP system. For the detection of knee OA, the SDM based system a

This paper presents two methods for calculating competence of a classifier in the feature space. The idea of the first method is based on relating the response of the classifier with the response obtained by a random guessing. The measure of competence reflects this relation and rates the classifier with respect to the random guessing in a continuous manner. In the second method, first a probabilistic reference classifier (PRC) is constructed which, on average, acts like the classifier evaluated. Next the competence of the classifier evaluated is calculated as the probability of correct classification of the respective PRC. Two multiclassifier systems (MCS) were developed using proposed measures of competence in a dynamic fashion. The performance of proposed MCS's were compared against six multiple classifier systems using six databases taken from the UCI Machine Learning Repository and Ludmila Kuncheva Collection. The experimental results clearly show the effectiveness of the proposed dynamic selection methods regardless of the ensamble type used (homogeneous or heterogeneous). ©2010 IEEE.

This paper presents a measure of competence based on a randomized reference classifier (RRC) for classifier ensembles. The RRC can be used to model, in terms of class supports, any classifier in the ensemble. The competence of a modelled classifier is calculated as the probability of correct classification of the respective RRC. A multiple classifier system (MCS) was developed and its performance was compared against five MCSs using eight databases taken from the UCI Machine Learning Repository. The system developed achieved the highest overall classification accuracies for both homogeneous and heterogeneous ensembles. © 2010 IEEE.

The paper presents a concept of hand movements recognition on the basis of EMG signal analysis. Signal features are represented by coefficient of autoregressive (AR) model, and as classifier the original multiclassifier systems with dynamic ensemble selection are applied. The performance of the proposed methods was experimentally compared against three classifiers using real datasets. The systems developed achieved the highest overall classification accuracies demonstrating the potential of dynamic classifier selection for recognition of EMG signals. ©2010 IEEE.

The paper presents a concept of bio-prosthesis control via recognition of user intent on the basis of myopotentials acquired of his body. We assume that in the control process each prosthesis operation consists of specific sequence of elementary actions. The multiclassifier systems with fusion/selection strategy based on competence function are applied to the recognition of patient's intent. Experimental investigations of the proposed multiclassifiers for real data are performed and results are discussed. Classification results obtained for three simple fusion methods and one multiclassifier system are used for a comparison. ©2009 IEEE.

This paper presents a method for combining classifiers in a tree structure, where each node of the tree contains single hypothesis trained in respective region of the feature space. All base classifiers are then combined using weighted average. Majority vote and Newton-Raphson numerical optimization are used for fitting the coefficients in the additive model. Two loss functions (quadratic and boosting-like exponential) as well as new splitting criteria for inducing the tree are examined within proposed framework. The idea of combining classifiers in a tree structure is then compared with other iteratively built classifiers: Adaboost.MH and MART (multiple additive regression trees). The experiments were conducted with the usage of well-known databases from the UCI Repository and the ELENA project. © 2008 IEEE.