Marcin Wolski

Differences in trabecular bone texture between knees with and without radiographic osteoarthritis detected by directional fractal signature method

OBJECTIVE To evaluate differences in tibial trabecular bone (TB) texture between subjects with and without radiographic knee osteoarthritis (OA) using a variance orientation transform (VOT) method. DESIGN Subjects with knee OA (Kellgren & Lawrence grade > or =2) and controls without OA (both n=26, seven women) were matched by sex, age, body mass index and compartment. The VOT method was applied to TB X-ray images and fractal signature and dimension in horizontal (FS(H), FD(H)) and vertical (FS(V), FD(V)) directions and along the roughest part of TB (FS(Sta), FD(Sta)), texture aspect ratio (Str) and signature (StrS), and mean FD (FD(MEAN)) were calculated. The VOT method was compared against an augmented Hurst orientation transform (HOT) method using paired t tests, intraclass correlation coefficients (ICCs) and coefficients of variation (CVs%). Longitudinal sensitivity to OA bone changes was not assessed. RESULTS For the reproducibility of texture parameters, ICCs were >0.75 and CVs% were <8.2% for both methods. Compared with controls, FD(MEAN), FD(H), FD(V) and FD(Sta) for OA knees were lower (P<0.001), while Str was higher in both medial (P=0.03) and lateral (P=0.02) compartments. FS(H), FS(Sta) were lower for OA knees than for controls at sizes 0.3-0.7 mm (P<0.001) in both compartments. In lateral compartment, FS(V) for OA knees was lower than for controls at sizes 0.3-0.5 mm (P<0.001) and 0.55-0.70 mm (P<0.02), while in medial compartment at sizes 0.3-0.7 mm (P<0.001). Compared with controls, StrS for OA knees was higher at sizes 0.3, 0.55-0.70 mm in medial (P<0.03) and lateral (P<0.04) compartments. CONCLUSIONS The VOT method is comparable to HOT method in the reproducibility of texture parameters and the ability to discriminate between non-OA and OA TB textures. However, unlike the HOT method, it quantifies texture roughness along the roughest part of the tibial bone, texture anisotropy at individual trabecular sizes and it works over a larger range of trabecular sizes. The VOT method may be a valuable tool for studying OA changes in TB.

Directional fractal signature analysis of trabecular bone: evaluation of different methods to detect early osteoarthritis in knee radiographs.

Abstract 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.

Trabecular bone texture detected by plain radiography and variance orientation transform method is different between knees with and without cartilage defects

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 <2) matched on sex, BMI, age, knee compartment, and meniscectomy where cases (n = 28) had cartilage defects (grade ≥2) and controls (n = 28) had no cartilage defects (grade <2). Cartilage defects were assessed from MRI using validated methods. The VOT was applied to TB regions selected on medial and lateral compartments in knee X‐rays and fractal signatures (FS) in the horizontal (FSH) and vertical (FSV) directions, and along the roughest part of TB (FSSta) and texture aspect ratio signatures (StrS), at different trabecular image sizes (0.30–0.70 mm) were calculated. Compared with controls, FSV for cases were higher (p < 0.011) at image sizes 0.30–0.40 mm and 0.45–0.55 mm in the medial compartment. In the lateral compartment, FSH and FSSta for cases were higher (p < 0.028) than those for controls at 0.30–0.40 mm and 0.45–0.55 mm, while FSV was higher (p < 0.02) at 0.30–0.40 mm. TB texture roughness was greater in subjects with cartilage defects than in subjects without, suggesting thinning and fenestration of TB occur early in OA and that the VOT identifies changes in TB in knees with early cartilage damage. No differences in StrS (p > 0.05) were found.

Directional Fractal Signature Analysis of Self-Structured Surface Textures

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.

Novel Directional Blanket Covering Method for Surface Curvature Analysis at Different Scales and Directions

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.

Automated selection of bone texture regions on hand radiographs : Data from the Osteoarthritis Initiative

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.

Texture analysis of self-structured surfaces in formation process using directional fractal signature method

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.