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Background Trabecular bone texture analysis (TBTA) has been identified as an imaging biomarker that provides information on trabecular bone changes due to knee osteoarthritis (KOA). Consequently, it is important to conduct a comprehensive review that would permit a better understanding of this unfamiliar image analysis technique in the area of KOA research. We examined how TBTA, conducted on knee radiographs, is associated to (i) KOA incidence and progression, (ii) total knee arthroplasty, and (iii) KOA treatment responses. The primary aims of this study are twofold: to provide (i) a narrative review of the studies conducted on radiographic KOA using TBTA, and (ii) a viewpoint on future research priorities. Method Literature searches were performed in the PubMed electronic database. Studies published between June 1991 and March 2020 and related to traditional and fractal image analysis of trabecular bone texture (TBT) on knee radiographs were identified. Results The search resulted in 219 papers. After title and abstract scanning, 39 studies were found eligible and then classified in accordance to six criteria: cross-sectional evaluation of osteoarthritis and non-osteoarthritis knees, understanding of bone microarchitecture, prediction of KOA progression, KOA incidence, and total knee arthroplasty and association with treatment response. Numerous studies have reported the relevance of TBTA as a potential bioimaging marker in the prediction of KOA incidence and progression. However, only a few studies have focused on the association of TBTA with both OA treatment responses and the prediction of knee joint replacement. Conclusion Clear evidence of biological plausibility for TBTA in KOA is already established. The review confirms the consistent association between TBT and important KOA endpoints such as KOA radiographic incidence and progression. TBTA could provide markers for enrichment of clinical trials enhancing the screening of KOA progressors. Major advances were made towards a fully automated assessment of KOA.

Wear dictates the machine performance, its reliability, and ultimately its lifetime. It is therefore important to know what is the wear status of the machine. The assessment can be done visually which is costly and not always possible (e.g. engines). Therefore, the development of an automated analysis and classification system of worn surfaces is highly desirable. Before such system is developed, surface characterization and classification methods must be first developed and tested. In this paper, we show how the recently developed methods, i.e. directional blanket covering and directional blanket covering curvature, have been adapted to solve this problem. The methods, unlike current standards/methods, measure surface roughness and curvature complexities at individual scales and directions. The methods’ ability to differentiate between microscopy images of abrasive and adhesive wear surfaces was evaluated. Minute differences between the two wear modes were detected. This could be of importance in machine condition diagnostic since abrasive and adhesive wear account for most of the machine failures.

Surface curvature affects adhesion forces, deformations of surfaces in contact, leakage of mechanical seals, friction, wear, paintability, and electrical conductivity. However, potential benefits of surface curvature have not yet been fully utilized. One problem is the lack of comparison data helping to make an informed decision on the selection of curvature characterization method. In this paper, five multiscale curvature characterization methods, namely Nowicki, Bigerelle-Nowicki, Gleason-Heron, Kalin, and Bartkowiak are compared. The comparison was conducted on large image databases of computer-generated fractal surfaces, sine waves and real engineering surfaces. Specifically, the methods were evaluated for their ability to differentiate between surfaces that: (i) exhibit increasing curvature complexity, (ii) have varying curvatures at a single scale, and (iii) represent minute multiscale curvature changes encountered in real engineering applications. The results obtained indicate that the Bigerelle-Nowicki method exhibits the best overall performance.

From the industrial view point, an automated classification system of worn surfaces is highly desirable for the monitoring and prediction of the operational health status of machines and their components. Optical microscopy images of abrasive and adhesive wear surfaces were obtained and analyzed using recently developed directional blanket covering (DBC) and DBC curvature (DBCC) methods. As these methods have the unique ability of to measure the surface roughness and curvature complexity at individual scales and directions, minute differences have been detected. In the present study, both DBC and DBCC methods were evaluated in differentiating between surfaces generated under abrasive, adhesive, and corrosive wear modes under different operating conditions, i.e., exhibiting different wear severity. The wear surfaces were imaged using an optical microscope and a confocal surface profilometer. Results obtained showed that the methods can detect minute differences between the wear modes and different wear severity, regardless of the imaging technique used. This is an important step in the development of machine diagnostic and prognostic systems/tools based on the images of worn surfaces.

Summary Objective To examine associations between trabecular bone (TB) texture and magnetic resonance imaging (MRI)-defined osteophytes in finger joints without radiographic osteoarthritis (OA) using an augmented variance orientation transform (AVOT) method. Design In a cross-sectional study, associations of the TB texture and osteophytes were examined in 21 women with mean (standard deviation) age of 69.9 (5.3) from the Oslo Hand OA cohort. The AVOT was applied to distal and proximal TB regions selected on hand radiographs of the subjects. The regions were adjacent to 57 finger joints (24 distal and 33 proximal interphalangeal) without radiographic OA (Kellgren–Lawrence [KL] grade 0), without MRI-defined joint space narrowing (JSN), bone marrow lesions (BMLs), attrition, erosion, cysts, sclerosis, malalignment (all MRI grades 0) and without or only with mild synovitis (MRI grade 0 or 1). Bone texture parameters were calculated: mean fractal dimension (FD MEAN ), FDs in the horizontal (FD H ) and vertical (FD V ) directions, and along the roughest part (FD Sta ). Associations between the parameters categorized into groups using tertiles and osteophytes were evaluated using logistic regression adjusted for age. Results Lower FD Sta and FD V in the distal TB regions were associated with increased odds of MRI-defined osteophytes ( P Conclusions Lower mean roughness and lower roughness in vertical and roughest directions of the proximal TB texture are associated with MRI-defined osteophytes in finger joints without radiographic OA. These findings suggest that TB texture may be a useful marker for detecting early hand OA.

Surface topography can affect the tribological performance in many applications. The influence of surface roughness and topographic orientation on friction and wear in dry and oil lubricated steel vs steel and diamond-like carbon (DLC) coated steel vs DLC coated steel sliding contacts was investigated. The surfaces had a centerline average roughness (Ra) between 0.004 and 0.11 μm and a topographic groove orientation of 0°, 45° and 90° with respect to the sliding direction. Tests conducted showed that the strongest effect of the orientation of the topographic directions occurs in dry sliding DLC vs DLC contacts. A super-lubricious DLC surface layer was efficiently formed, exhibiting low friction coefficients of 0.04 for smooth surfaces, and rough surfaces with grooves oriented along the sliding direction. The process of surface layer formation was most severely disturbed for average surfaces roughness with grooves oriented at 45° to the sliding direction. For these surfaces, the coefficient of friction was about five times higher (0.21) as compared to smooth samples (0.04). In dry sliding steel contacts, the coefficient of friction decreased with increasing surface roughness. On the other hand, the coefficient of friction increased with surface roughness in oil lubricated steel contacts. Adding a synthetic oil into the DLC vs DLC sliding contact prevented the formation of the super-lubricious surface layer. In tests with rotational steel vs steel sliding the friction coefficient was about 10% lower than in test with linear reciprocal sliding but was higher in DLC vs DLC contacts.

Diamond-like carbon (DLC) coated surfaces exhibit anisotropic and multi-scale characteristics, i.e., their roughness change with both scale and direction. However, most currently used standard surface characterisation parameters and methods work well only with isotropic surfaces at a single scale. This problem can be overcome by variance orientation transform (VOT) and directional blanket covering (DBC) methods. Both methods calculate fractal signatures (FSs) in different directions allowing for detailed measurement of roughness of anisotropic and multiscale surfaces. FS is a set of fractal dimensions (FDs) at individual scales, and FD is a measure of surface roughness. High FD values mean rougher surfaces. Unlike other directional FSs methods, e.g., VOT, the DBC method automatically selects scales of calculations. In this study, the DBC method was used to analyse surface topography images of DLC coated and uncoated bearing steel discs of increasing roughness. Its ability to differentiate between two groups of surfaces is evaluated. The results obtained showed that the DBC method can detect differences in roughness at different scales and directions between the DLC coated and uncoated surfaces. This work could lead to applications of the DBC method in modelling of wear and friction behaviour of DLC coated and uncoated surfaces at different scales.

Arthroscopic partial meniscectomy (APM) may lead to changes in underlying trabecular bone (TB) structure potentially promoting the development of knee joint osteoarthritis. Our aim was to investigate if there are early changes occurring in tibial subchondral TB texture in the leg undergoing medial APM compared with the unoperated non-injured contra-lateral leg. The bone texture was measured as the medial-to-lateral ratio of fractal dimensions (FD) calculated for regions selected on weight-bearing anteroposterior tibiofemoral x-rays. Twenty-one subjects before and 12 months after APM were included from 374 patients scheduled for unilateral medial APM. The medial-to-lateral ratio was calculated for horizontal, vertical, and roughest FDs respectively. Higher FD means higher bone roughness. Each FD was calculated over a range of scales using a variance orientation transform method. Mean values of medial-to-lateral horizontal FD calculated for APM knees at follow-up were higher than those at baseline. For unoperated knees the values were lower. The difference in the horizontal FD change from baseline to follow-up between APM and contra-lateral legs was 0.028 (95% CI, 0.004-0.052). The bone roughness changes may reflect the increase in peak knee adduction moment (KAM) and KAM impulse during walking reported for the same cohort in a previous study. They may also reflect early signs of osteoarthritis development and thus, we speculate that individuals with increased bone texture roughness ratio after APM might be at higher risk of knee osteoarthritis development.

Surface texture affects adhesion, friction and wear. A key component in the studies of these effects is a multiscale and directional characterization of surfaces. However, this task is beyond the ability of standard methods currently used. Methods based on fractal geometry, quantifying the surface topography at individual scales and directions, have been developed to overcome this problem. In this study, we propose a unified approach for surface texture analysis comprising three steps: directional surface representation, directional surface dimension and computer implementation. To address longstanding issues of texture analysis of surfaces with directionality and studies of surface texture effects on contact behaviour of rough surfaces, a directional blanket curvature covering method was used. Peak curvature signatures (sets of peak dimensions) were calculated. Higher value of peak dimension represents higher peak curvature complexity. Isotropic (sandblasted) and anisotropic (abraded) titanium alloy (Ti–6Al–4V) surface images were generated. Statistically significant differences in peak dimension were found between isotropic and anisotropic surfaces at scales of 15 μm and 20 μm. Contact areas and pressures were calculated between rigid flat plates pressed against Ti–6Al–4V rough surfaces. For low loads at a scale of 15 μm, contact areas decreased with dimension, while at scale of 25 μm they decreased and then increased. Changes in contact pressures exhibited trends opposite to the areas. For high loads, there are virtually no changes. Results showed that the curvature covering method detects minute differences in surface anisotropy and allows for detailed study of contact behaviour.

The effects of nanoscale ripple texture on the film thickness and friction in elastohydrodynamically lubricated (EHL) contacts were investigated through ball-on-disc experiments and numerical simulations of line contacts. The texturing was produced by femtosecond LASER irradiations and the ripple texture was in the form of sinusoidal waviness with nanoscale amplitudes and wavelengths. The experimental and numerical results indicate that the orientation of the ripples with respect to the entrainment direction has little to no effect on their capability to form a lubricating film. In the EHL regime, the ripples were found to reduce the central and minimum film thickness by half of their peak-to-peak amplitude as compared to a smooth contact. The transition from EHL to mixed lubrication regime was attributed to micro-EHL effects although the subsequent friction increase was found to be largely due to the onset of asperity contacts. In the mixed lubrication regime, the coefficient of friction was mainly determined by surface roughness and its value increased with an increase in the ripple amplitude.

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.

Manual selection of finger trabecular bone texture regions on hand X-ray images is time-consuming, tedious, and observer-dependent. Therefore, we developed an automated method for the region selection. The method selects square trabecular bone regions of interest above and below the second to fifth distal and proximal interphalangeal joints. Two regions are selected per joint (16 regions per hand). The method consists of four integral parts: (1) segmentation of a radiograph into hand and background, (2) identification of finger regions, (3) localization of center points of heads of distal phalanges and the distal interphalangeal, proximal interphalangeal, and metacarpophalangeal joints, and (4) placement of the regions of interest under and above the distal and proximal interphalangeal joints. A gold standard was constructed from regions selected by two observers on 40 hand X-ray images taken from Osteoarthritis Initiative cohort. Datasets of 520 images were generated from the 40 images to study the effects of hand and finger positioning. The accuracy in regions selection and the agreement in calculating five directional fractal parameters were evaluated against the gold standard. The accuracy, agreement, and effects of hand and finger positioning were measured using similarity index (0 for no overlap and 1 for entire overlap) and interclass correlation coefficient as appropriate. A high accuracy in selecting regions (similarity index ≥ 0.79) and a good agreement in fractal parameters (interclass correlation coefficient ≥ 0.58) were achieved. Hand and finger positioning did not affect considerably the region selection (similarity index ≥ 0.70). These results indicate that the method developed selects bone regions on hand X-ray images with accuracy sufficient for fractal analyses of bone texture.

Human skeletal remains may provide information about health status and injuries from hundreds of thousands of years ago. However, the bones in living individuals have the potential to tell us something about future health as well. In the current issue of Arthritis & Rheumatology, Kraus et al report data of prediction of osteoarthritis (OA) progression using tibial subchondral trabecular bone texture (TBT) from plain knee radiographs (1). The ability of medial TBT was assessed to predict pain progression (an increase in WOMAC score), radiographic OA progression (a loss in joint space width [JSW]), and OA status (a combination of pain and radiographic OA progression), over 24-48 months. This article is protected by copyright. All rights reserved.

Roughness and curvature of diamond-like carbon (DLC) surface coatings change with both scale and direction of a measurement. However, the changes are not detected by currently used standard parameters which are designed to work with isotropic surfaces at a single scale, thus providing only a limited information about multiscale and directional roughness and curvature. The problem of detailed roughness characterization of DLC-coated surfaces has been addressed in our previous work [Wolski et al. Multiscale characterization of 3D surface topography of DLC-coated and uncoated surfaces by directional blanket covering method. Wear 2017:388–389:47–56]. However, surface curvature description still remains an unresolved issue. To overcome this shortcoming, a directional blanket curvature covering (DBCC) method was developed. The method calculates curvature, peak and valley dimensions which quantify multiscale and directional curvature complexity of surface topography, peaks and valleys, respectively. Higher values of the dimensions represent higher complexity. In the current study, the DBCC method was used to analyse DLC-coated and uncoated bearing steel samples with increasing roughness and curvature. Its ability to discriminate between these two groups of surfaces was evaluated. Results showed that the method could detect minute changes in surface curvature at individual scales and directions. The method would be of interest to those who design wear-resistant systems and surfaces.

Fluid inertia can have a major effect on the pressure distribution in hydrodynamic bearings with surface texturing. So far, however, the effect has been ignored in the simulation of finite bearings. In this work, we develop a spectral element solver for the Navier–Stokes equations specifically tailored to slider finite bearings textured with multiple spherical dimples. Using the solver, we studied the effect of inertia on the load-carrying capacity for 90 different bearing configurations. Our results show that the spatial arrangement of dimples has a significant impact on the inertia effect. For the Reynolds number 50 and the ratio 10 of dimple length to dimple depth, a change from full to partial texturing reduced the effect by roughly 85%.

The effects of surface roughness and topographical orientation on surface stresses influencing wear have been investigated for diamond like carbon (DLC) coated steel surfaces with three levels of surface roughness in the range of 0.004–0.11 μm R a values, and with topographical groove orientations of 0°, 45° and 90°. A novel multiscale numerical finite element method (FEM) model was developed to integrate the layered and microstructural material features with the grooved topography. Fractal geometry and a surface voxelisation based approach were utilised to derive 3D surface topography. The surface texture representation includes: fractal signatures, which are sets of fractal dimensions calculated at individual scales in different directions, texture aspect ratio describing surface anisotropy, and texture direction signatures calculated by the variance orientation transform (VOT) method. The simulations show details of the main topographical orientation effects on local stresses affecting wear as they appear at a single scratch test with a spherical diamond ball and in a self-mated sliding situation of two rough surfaces. The 45° sliding direction in relation to grooves resulted in a mixed state of surface loading in the scratch test contact. In the complex state of stress-strain within the roughness peaks the overall tensile stress decreased, leading to greater surface resistance to cracking as compared to 0° and 90° directions. Model based calculations showed that the surface structure was about four times more rigid in the direction of grooves as compared to the more flexible behaviour in the perpendicular direction. This behaviour was empirically confirmed. The numerical calculations of rough vs rough sliding surface include real surface topographical features at various scales, material microstructural features down to nano-scale and topographical-microstructural interaction features. This approximation is thus more comprehensive than the classical approach. The real area of contact was 15–30% of the apparent contact area. The macro-topography dominated the tendency for surface cracking and plastic deformation, which is influencing on both wear and friction, while the micro-topographical features contributed to cracking and deformation by less than 40%.

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.

Efficient numerical methods are essential in the analysis of finite hydrodynamic bearings with surface texturing. This is especially evident in optimization and parametric studies where the discretization and integration methods are used to solve the governing two-dimensional Reynolds equation multiple times. Performance comparison studies of the methods are thus required to select the method that is most suitable for a particular bearing geometry. In this work, we conduct a comprehensive and systematic comparison of typical implementations of the finite difference, finite volume, finite element and spectral element discretization methods together with the Newton–Cotes formula and Gauss quadratures for hydrodynamic bearings governed by the two-dimensional Reynolds equation. The methods were evaluated by comparing the approximation errors in the calculation of the maximum pressure, load capacity, coefficient of friction, and minimum film thickness for parallel and convergent bearings textured with elliptical grooves or trapezoidal dimples. The number of degrees of freedom required by the methods to achieve the error cut-off values of 5%, 1% and 0.1% were calculated. Our results demonstrate that the spectral element method uses up to 72 times fewer degrees of freedom than the other methods for the same cut-off values. Also, our study revealed that the shape of the groove/dimple and the bearing convergence ratio can have a significant effect on the approximation errors of the numerical methods used. Specifically, for piecewise-linear texture features (e.g. trapezoidal dimples) and positive convergence ratio it is easier, for the methods, to accurately approximate the solution. In such cases, the finite volume and finite element methods are reasonable choices and provide a good trade-off between the ease of implementation and approximation errors. The worse performance was observed for the finite difference and thus this method is not recommended when the computational efficiency and the accuracy of results are of importance.

The range of applications for self-structured surfaces is growing. They are used to increase wear resistance, reduce friction and corrosion, and also used in design of biosensors and innovative coatings. However, to effectively manufacture these surfaces on a large scale, methods for their texture characterisation/description are required. Currently, we do not have any effective and accurate methods to characterise these surfaces. This is severely hindering their wider applications and further developments in this area. The texture of self-structured surfaces, like any texture of any other surface, would need to be characterised/described during the formation (production) process and for specific applications. During formation process, the self-structured surfaces change texture roughness and directionality. These changes are gradual, complex and occur over many scales. In this work, a recently developed method, called an augmented blanket with rotating grid method, is applied to microscopic images of real self-structured surface textures. Groups of isotropic and anisotropic texture images were analysed. In the first group the textures were formed through the growth of nanorods on indium oxide substrates while in another the laser beam irradiation was used to treat the polymer films. Results obtained showed that the augmented blanket with rotating grid method accurately quantifies minute decreases in roughness of isotropic surfaces and changes in roughness with directions of anisotropic surfaces observed during the formation process.

Surface texturing has been demonstrated to improve tribological performance of hydrodynamic bearings. Because the texturing increases complexity of a surface, numerical methods are required. However, no comparison study has so far been conducted to determine which methods are most accurate with the least number of grid/mesh points. Knowing this would allow for the analysis and optimisation of bearings with complex geometries. In this work, performance of four discretisation methods (finite difference, finite element, finite volume and spectral element (SE)) in approximating the pressure function and three integration methods (Newton–Cotes formulas and Gauss quadrature) in approximating the load capacity, coefficient of friction and film height was evaluated in a systematic manner. Three slider bearing geometries were used: inclined surface without texturing and two parallel surfaces textured with trapezoidal and quadratic dimples. For the evaluation, pressure function, load capacity, coefficient of friction were calculated analytically using the Reynolds equation. Differences between the analytical values and their approximations produced by the numerical methods were calculated versus the number of grid/mesh points. The numbers of points were determined for the differences below 5, 1 and 0.1 %. Results showed that the SE method and the Gauss quadrature were most accurate regardless of the bearing geometry and used up to 28 times fewer points as compared to other methods.

A new method, called augmented blanket with rotating grid (ABRG), has been proposed in our recent work on characterizing roughness and directionality of self-structured surface textures. This is the first method that calculates fractal dimensions (FDs) at individual scales and directions for the entire surface image data and does not require the data to be Brownian fractal. However, before the ABRG method can be used in real applications, effects of atomic force microscope (AFM) imaging conditions on FDs need to be evaluated first. In this paper, computer-generated AFM images with three different resolutions, 48 combinations of tip radii and cone angles, and 15 noise levels were used in the tests. The images represent isotropic self-structured surface textures with small, medium and large motif sizes, and anisotropic surfaces exhibiting two dominating directions. For isotropic surfaces, the ABRG method is not significantly affected (i.e. FDs changes

The curvature of surface topography is quantified in this study using a newly developed directional blanket covering curvature (DBCC) method. The novel method addresses a long-standing problem of a measurement of a local surface curvature at individual scales and directions. The curvature data are obtained using the first and second derivations of the quadratic polynomial fitted to the local surface profile extracted from the surface height image data at individual scale and direction. The range of scales is set between an instrument spatial resolution and 1/10 of the image shortest size. Using the surface curvature data, three parameters, i.e. curvature, peak and valley dimensions, are calculated as the slopes of lines fitted to data point subsets of log–log plots of surface areas (differences between dilated and eroded surface curvature matrices) against scales of calculation. The dimensions quantify directional changes in the overall curvature and the curvature of peaks and valleys at individual scales. The scale corresponds to the centre of the subset. A flat surface criterion based on the surface areas was also proposed. Using the criterion, a flat surface was identified in computer images of dome, corrugated plate and fractal surface. The DBCC method was applied to computer-generated fractal surfaces with increasing curvature complexity, sine waves with decreasing curvature at single scale and microscope images of isotropic (sandblasted) and anisotropic (ground) surfaces of titanium plates. Results showed that the method is accurate in the measurement of surface curvature and the detection of minute changes occurring in the curvature of real surfaces over a wide range of scales.

PurposeTo explore the association of baseline trabecular bone structure with incident tibiofemoral (TF) osteoarthritis (OA) and with increase in joint space narrowing (JSN) score.MethodsThe Multicenter Osteoarthritis Study (MOST) includes subjects with or at risk for knee OA. Knee radiographs were scored for Kellgren–Lawrence (KL) grade and JSN at baseline, 30, 60 and 84 months. Knees (KL ≤ 1) at baseline were assessed for incident OA (KL ≥ 2) and increases in JSN score. For each knee image at baseline, a variance orientation transform method (VOT) was applied to subchondral tibial bone regions of medial and lateral compartments. Seventeen fractal parameters were calculated per region. Associations of each parameter with OA incidence and with medial and lateral JSN increases were explored using logistic regression. Analyses were stratified by digitized film (DF) vs computer radiography (CR) and adjusted for confounders.ResultsOf 894 knees with CR and 1158 knees with DF, 195 (22%) and 303 (26%) developed incident OA. Higher medial bone roughness was associated with increased odds of OA incidence at 60 and 84 months and also, medial and lateral JSN increases (primarily vertical). Lower medial and lateral anisotropy was associated with increased odds of medial and lateral JSN increase. Compared to DF, CR had more associations and also, similar results at overlapping scales.ConclusionBaseline trabecular bone texture was associated with incident radiographic OA and increase of JSN scores independently of risk factors for knee OA. Higher roughness and lower anisotropy were associated with increased odds for radiographic OA change.

Surface texturing has a potential to become a cost effective and easy way to improve the tribological performance of lubricated interfacing surfaces. Effects of surface textures on the performance of machine elements as frictional pairs have been investigated over the past two decades. However, despite this research only a limited number of analytical solutions have been proposed as the majority of studies have been experimental and results obtained have not been optimal. This is because the commonly used surface characterization methods are not able to quantify surface textures over a range of scales at different directions and the optimization methods used work only for relatively simple textures and specific constraints imposed on pressure, film thickness, sliding velocity and lubricant rheology. Previous studies have addressed these issues, to some degree, by developing directional fractal signature methods and unified computational approach for texture optimization. In this article, recent advancements in the development of fractal methods and optimization of surface textures are presented.

In this paper, a measure of competence based on random classification (MCR) for classifier ensembles is presented. The measure selects dynamically (i.e. for each test example) a subset of classifiers from the ensemble that perform better than a random classifier. Therefore, weak (incompetent) classifiers that would adversely affect the performance of a classification system are eliminated. When all classifiers in the ensemble are evaluated as incompetent, the classification accuracy of the system can be increased by using the random classifier instead. Theoretical justification for using the measure with the majority voting rule is given. Two MCR based systems were developed and their performance was compared against six multiple classifier systems using data sets taken from the UCI Machine Learning Repository and Ludmila Kuncheva Collection. The systems developed had typically the highest classification accuracies regardless of the ensemble type used (homogeneous or heterogeneous).

Currently available directional fractal signature (DFS) methods are not suited for self-structured surface textures since they base on the assumption of Brownian fractal or they do not use the entire image data in calculation. To address these difficulties, two new DFS methods were developed in this study, i.e., an augmented blanket with rotating grid (ABRG) method and a blanket with shearing image (BSI) method. The performance of these methods in measuring surface roughness and directionality, the capacity for quantifying multi-patterned textures, and the ability to detect differences between textures of self-structured surfaces were evaluated. The methods were compared against a blanket with rotating grid (BRG) method. Computer-generated images of self-structured surface textures with different roughness, directions and patterns, and atomic force microscope images of real self-structured surfaces were used. The computer texture images were generated using a specially developed motif-based texture generator. Results obtained showed that the ABRG method is more accurate and reliable than the BRG and BSI methods.

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 semi-quantitative 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. The predictive ability of the system needs to be further validated.

Textured surfaces can significantly improve the performance of hydrodynamic bearings. However, there is no generally accepted method for their accurate and automated 3D characterization. A promising solution to this problem is partition iterated function system (PIFS) model, which encapsulates information about 3D topography of textured surfaces. However, some loss in surface details can occur. Therefore, before PIFS could be used, effects of this information loss on load and friction need to be investigated. In this study, this issue was addressed using a textured hydrodynamic pad bearing. The results obtained showed that PIFS models might become useful in characterization of textured surfaces.

The objective of this work is to evaluate differences in trabecular bone (TB) texture between subjects with and without tibiofemoral cartilage defects using a variance orientation transform (VOT) method. A case-control study was performed in subjects without radiographic knee osteoarthritis (OA) (K&L grade 0.05) were found.

There is no generally accepted method that could provide a full description of 3D surface topography (ST). Most of the currently used methods work well with isotropic surfaces at a single scale. Recently new method, called a variance orientation transform (VOT), has been developed for ST characterization. In this work, the usefulness of the VOT and its sensitivity to minute changes in ST have been tested. Images of real engineering surfaces, surfaces of adhesive wear particles and trabecular bone were used for the tests. The results obtained show that the VOT can be useful in both engineering and medical applications.

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 binarymore » 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 ( 64x64 pixels). However, the measure is sensitive to changes in projection angle (>5 deg.), image anisotropy (>30 deg.), 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 achieved 78.8% classification accuracy and outperformed the WND-CHARM system (64.2%). Conclusions: The SDM is well suited for the classification of TB texture images in knee OA detection and may be useful for the texture classification of medical images in general.« less

In tribological applications, surface textures are used to increase load capacity and reduce friction losses in hydrodynamic lubricated contacts. However, there is no systematic, efficient and general approach allowing for the optimization of surface texture shapes to give an optimal performance. The work conducted is, in most cases, by “trial and error”, i.e. changes are introduced and their effects studied. This is time consuming and inefficient. In this paper, a unified computational approach to the optimization of texture shapes in bearings is proposed. The approach aims at finding the optimal texture shape that supports the maximum load and/or minimizes friction losses in one dimensional hydrodynamic bearings. The texture shape optimization problem is transformed into a nonlinearly constrained mathematical programming problem with general constraints that can be solved using optimal control software. Load-carrying capacity or friction force of a bearing becomes an objective functional that is maximized or minimized, subject to: (i) any Reynolds equations given by first order ordinary differential equations, (ii) pressure boundary conditions and (iii) functions/parameters that define the surface texture shape. This newly developed approach is demonstrated on examples of parallel textured hydrodynamic bearings. The effects of non-Newtonian fluids, cavitation and viscosity varying with temperature are considered.

There is ongoing research directed towards the development of cheap and reliable decision support systems for the detection and prediction of osteoarthritis (OA) in knee joints. Fractal analysis of trabecular bone texture X-ray images is one of the most promising approaches. It is cheap and non-invasive. However, difficulties arise when the fractal signature methods are used to quantify bone roughness and anisotropy on individual scales. This is because the fractal methods are able to quantify bone texture only in the vertical and horizontal directions, and previous studies showed that OA bone changes can occur in any direction. To address these difficulties, three directional fractal signature methods were developed in this study, i.e. a fractal signature Hurst orientation transform (FSHOT) method, a variance orientation transform (VOT) method, and a blanket with rotating-grid (BRG) method. These methods were tested and the best performing method was selected. Unlike other methods, the newly developed techniques are able to calculate fractal dimensions (FDs) on individual scales (i.e. fractal signature) in all possible directions. The accuracy of the methods developed in measuring texture roughness and anisotropy on individual scales was evaluated. The effects of imaging conditions such as image noise, blur, exposure, magnification, and projection angle and the effects of translation of the bone region of interest on texture parameters were also evaluated. Computer-generated fractal surface images with known FDs and X-ray images obtained for a human tibia head were used. Results obtained show that the VOT method performs better than the FSHOT and BRG methods.

Surface texturing is a new technology aiming at reducing friction in operating machinery. Different surface features of varying shape and density are artificially introduced onto the existing surfaces. The methods currently available for the 3-D characterization and description of these surfaces are inadequate. Reliable surface description is necessary for the optimization of those surfaces and quality control during production. In this paper possible ways of solving the problems associated with 3-D description and optimization of textured surfaces are outlined and discussed. The problems associated with the development of automated classification systems for textured surfaces are also presented.

Purpose: This study investigated the relationship between muscular strength about the knee and knee joint moments during gait in patients who had undergone arthroscopic partial meniscectomy (APM). Methods: One hundred and two APM patients and 42 age-matched nonoperated controls underwent strength testing and three-dimensional gait analysis. Patients were divided into weak and normal subgroups and compared with controls for spatiotemporal, kinematic, and kinetic gait parameters. Results: Spatiotemporal parameters, kinematics, and sagittal plane kinetics were similar between APM patients and controls. The APM group displayed weaker concentric knee extension and flexion strength compared with controls. The weak APM subgroup had an increased average and peak knee adduction moments over stance compared with the APM subgroup with normal strength levels and controls. The normal strength APM subgroup had a larger peak knee adduction moment in early stance compared with controls. Conclusion: Achieving normal lower limb muscle strength following APM appears important to resume normal frontal plane loading of the knee while walking.

Osteoarthritic (OA) changes in knee joints can be assessed by analyzing the structure of trabecular bone (TB) in the tibia. This analysis is performed on TB regions selected manually by a human operator on x-ray images. Manual selection is time-consuming, tedious, and expensive. Even if a radiologist expert or highly trained person is available to select regions, high inter- and intraobserver variabilities are still possible. A fully automated image segmentation method was, therefore, developed to select the bone regions for numerical analyses of changes in bone structures. The newly developed method consists of image preprocessing, delineation of cortical bone plates (active shape model), and location of regions of interest (ROI). The method was trained on an independent set of 40 x-ray images. Automatically selected regions were compared to the ''gold standard'' that contains ROIs selected manually by a radiologist expert on 132 x-ray images. All images were acquired from subjects locked in a standardized standing position using a radiography rig. The size of each ROI is 12.8x12.8 mm. The automated method results showed a good agreement with the gold standard [similarity index (SI)=0.83 (medial) and 0.81 (lateral) and the offset=[-1.78, 1.27]x[-0.65,0.26] mm (medial) and [-2.15, 1.59]x[-0.58, 0.52] mm (lateral)]. Blandmore » and Altman plots were constructed for fractal signatures, and changes of fractal dimensions (FD) to region offsets calculated between the gold standard and automatically selected regions were calculated. The plots showed a random scatter and the 95% confidence intervals were (-0.006, 0.008) and (-0.001, 0.011). The changes of FDs to region offsets were less than 0.035. Previous studies showed that differences in FDs between non-OA and OA bone regions were greater than 0.05. ROIs were also selected by a second radiologist and then evaluated. Results indicated that the newly developed method could replace a human operator and produces bone regions with an accuracy that is sufficient for fractal analyses of bone texture.« less

In this study, the automated classification system, developed previously by the authors, was used to classify wear particles. Three kinds of wear particles, fatigue, abrasive and adhesive, were classified. The fatigue wear particles were generated using an FZG back-to-back gear test rig. A pin-on-disk tribometer was used to generate the abrasive and adhesive wear particles. Scanning electron microscope (SEM) images of wear particles were acquired, forming a database for further analysis. The particle images were divided into three groups or classes, each class representing a different wear mechanism. Each particle class was first examined visually. Next, area, perimeter, convexity and elongation parameters were determined for each class using image analysis software and the parameters were statistically analysed. Each particle class was then assessed using the automated classification system, based on particle surface texture. The results of the automated particle classification were compared to both the visual assessment of particle morphology and the numerical parameter values. The results showed that the texture-based classification system was a more efficient and accurate way of distinguishing between various wear particles than classification based on size and shape of wear particles. It seems that the texture-based classification method developed has great potential to become a very useful tool in the machine condition monitoring industry.

Detailed characterization of 3D engineering surface topographies is still an unresolved problem. The reasons are that the majority of the real surfaces are anisotropic and multi-scale, i.e. their directionality and roughness change with the measurement scales. To solve this problem, a variance orientation transform (VOT) method was developed. It calculates fractal dimensions at individual scales, i.e. it calculates the fractal signature (FS) in all possible directions, addressing, in this way, the problems of surfaces’ multi-scale and anisotropic nature. However, the VOT method is not suited for the analysis of image sizes that are smaller than 48 × 48 pixels (e.g. images of wear particles surfaces, small surface defects, etc.). To redress this problem the VOT method was augmented so that it can calculate FSs for all images including those with small sizes. Previous study showed that the augmented VOT (AVOT) method is accurate in the analysis of hand x-ray images where the bone texture images are small (20 × 20 pixels). However, its usefulness in analysing small images of engineering surfaces has not yet been investigated. In the current study, we use range-images of different sizes (20 × 20 and 30 × 30 pixels) of polished (isotropic) and ground (anisotropic) steel plates. When applied to images of steel surfaces of different topography, the AVOT method has detected minute changes at different scales, undetectable by other commonly used surface characterization methods, between the surfaces. The results show that the method can be a valuable tool in characterization of small images of 3D engineering surfaces.

A new method, called directional blanket covering (DBC) method, was developed in this study to quantify the roughness of surfaces in a multi-scale manner. Unlike the ASME and ISO standards on surface texture that provide a single fractal dimension (FD, roughness measure) for the entire surface, the new method calculates FDs at individual scales and directions. Also, it is invariant to affine transformations of grey-scale levels of surface image. The method calculates FDs using slopes of lines fitted to data point subsets of log–log plots of relative surface areas (differences between surface volumes) against scales of calculation. The scales are ranking from an instrument spatial resolution to 1/10 of the image shortest size. Each FD calculated has its individual scale corresponding to the centre of the subset. A flat surface criterion based on the relative areas was proposed. Using the criterion, a flat surface was identified in computer images of circle, sine wave and fractal surface. The DBC method was applied to computer-generated fractal surfaces with increasing roughness and microscope images of isotropic (sandblasted) and anisotropic (ground) surfaces of steel plates. Results showed that the method is accurate in the measurement of surface roughness and the detection of minute changes in roughness of the steel surfaces over a wide range of scales.