Estimation of the 3D self-similarity parameter of trabecular bone from its 2D projection

Trabecular bone texture analysis of conventional radiographs in the assessment of knee osteoarthritis: review and viewpoint

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.

Computer-aided diagnosis systems for osteoporosis detection: a comprehensive survey

Computer-aided diagnosis (CAD) has revolutionized the field of medical diagnosis. They assist in improving the treatment potentials and intensify the survival frequency by early diagnosing the diseases in an efficient, timely, and cost-effective way. The automatic segmentation has led the radiologist to successfully segment the region of interest to improve the diagnosis of diseases from medical images which is not so efficiently possible by manual segmentation. The aim of this paper is to survey the vision-based CAD systems especially focusing on the segmentation techniques for the pathological bone disease known as osteoporosis. Osteoporosis is the state of the bones where the mineral density of bones decreases and they become porous, making the bones easily susceptible to fractures by small injury or a fall. The article covers the image acquisition techniques for acquiring the medical images for osteoporosis diagnosis. The article also discusses the advanced machine learning paradigms employed in segmentation for osteoporosis disease. Other image processing steps in osteoporosis like feature extraction and classification are also briefly described. Finally, the paper gives the future directions to improve the osteoporosis diagnosis and presents the proposed architecture. Graphical abstract.

Multifractal-based lacunarity analysis of trabecular bone in radiography

This study presents textural characterization techniques for effective osteoporosis diagnosis using bone radiograph images. The automatic classification of osteoporosis and healthy (control) cases using bone radiograph images in this work presents a major challenge as the images show no visual differences for both cases. The proposed work utilizes multifractals to characterize the trabecular bone texture in the radiographs. Initially, Holder exponents are computed, then Hausdorff dimensions are determined, which quantify the global regularity of the pixels. Finally, lacunarity is computed from the Hausdorff dimensions. Performance metrics show that estimating lacunarity from the Hausdorff dimensions, rather than the input image, directly helps in achieving better textural characterization of bone radiographs, leading to better performance in osteoporosis classification. The proposed lacunarity-based trabecular bone textural characterization method is compared with other multifractal-based methods for trabecular bone textural characterization, such as box-counting and regularization dimensions. The proposed method is also evaluated with the textural characterization of a bone radiograph challenge dataset to demonstrate its effectiveness compared to the other methods used in the challenge.

Rapid quantification of mitochondrial fractal dimension in individual cells

An improved technique for fractal characterization called the modified blanket method is introduced that can quantify surrounding fractal structures on a pixel by pixel basis without artifacts associated with scale-dependent image features such as object size. The method interprets images as topographical maps, obtaining information regarding the local surface area as a function of image resolution. Local fractal dimension (FD) can be quantified from the power law exponent derived from the surface area and image resolution relationship. We apply this technique on simulated cell images of known FD and compared the obtained values to power spectral density (PSD) analysis. Our method is sensitive to a wider FD range (2.0-4.5), having a mean error of 1.4% compared to 6% for PSD analysis. This increased sensitivity and an ability to compute regional FD properties enabled the discrimination of the differences in radiation resistant cancer cell responses that could not be detected using PSD analysis.

Current and Emerging Diagnostic Imaging-Based Techniques for Assessment of Osteoporosis and Fracture Risk

Osteoporosis is a metabolic bone disorder characterized by low bone mass, degradation of bone microarchitecture, and susceptibility to fracture. It is a growing major health concern across the world, especially in the elderly population. Osteoporosis can cause hip or spinal fractures that may lead to high morbidity and socio-economic burden. Therefore, there is a need for early diagnosis of osteoporosis and prediction of fragility fracture risk. In this review, state of the art and recent advances in imaging techniques for diagnosis of osteoporosis and fracture risk assessment have been explored. Segmentation methods used to segment the regions of interest and texture analysis methods used for classification of healthy and osteoporotic subjects are also presented. Furthermore, challenges posed by the current diagnostic tools have been studied and feasible solutions to circumvent the limitations are discussed. Early diagnosis of osteoporosis and prediction of fracture risk require the development of highly precise and accurate low-cost diagnostic techniques that would help the elderly population in low economies.

Automated skeletal tissue quantification in the lower leg using peripheral quantitative computed tomography

OBJECTIVE

In this paper we introduce a methodology for hard and soft tissue quantification at proximal, intermediate and distal tibia sites using peripheral quantitative computed tomography scans. Quantification of bone properties is crucial for estimating bone structure resistance to mechanical stress and adaptations to loading. Soft tissue variables can be computed to investigate muscle volume and density, muscle-bone relationship, and fat infiltration.

APPROACH

We employed implicit active contour models and clustering techniques for automated segmentation and identification of bone, muscle and fat at [Formula: see text], [Formula: see text], and [Formula: see text] tibia length. Next, we calculated densitometric, area and shape characteristics for each tissue type. We implemented our approach as a multi-platform tool denoted by TIDAQ (tissue identification and quantification) to be used by clinical researchers.

MAIN RESULTS

We validated the proposed method against reference quantification measurements and tissue delineations obtained by semi-automated workflows. The average Deming regression slope between the tested and reference method was 1.126 for cross-sectional areas and 1.078 for mineral densities, indicating very good agreement. Our method produced high average coefficient of variation (R ²) estimates: 0.935 for cross-sectional areas and 0.888 for mineral densities over all tibia sites. In addition, our tissue segmentation approach achieved an average Dice coefficient of 0.91 over soft and hard tissues, indicating very good delineation accuracy.

SIGNIFICANCE

Our methodology should allow for high throughput, accurate and reproducible automatic quantification of muscle and bone characteristics of the lower leg. This information is critical to evaluate risk of future adverse outcomes and assess the effect of medications, hormones, and behavioral interventions aimed at improving bone and muscle strength.

Subchondral tibial bone texture predicts the incidence of radiographic knee osteoarthritis: data from the Osteoarthritis Initiative

OBJECTIVES

To evaluate whether trabecular bone texture (TBT) parameters measured on computed radiographs (CR) could predict the onset of radiographic knee osteoarthritis (OA).

MATERIALS AND METHODS

Subjects from the Osteoarthritis Initiative (OAI) with no sign of radiographic OA at baseline were included. Cases that developed either a global radiographic OA defined by the Kellgren-Lawrence (KL) scale, a joint space narrowing (JSN) or tibial osteophytes (TOS) were compared with the controls with no changes after 48 months of follow-up. Baseline bilateral fixed flexion CR were analyzed using a fractal method to characterize the local variations. The prediction was explored using logistic regression models evaluated by the area under the receiver operating characteristic curves (AUC).

RESULTS

From the 344 knees, 79 (23%) developed radiographic OA after 48 months, 44 (13%) developed progressive JSN and 59 (17%) developed osteophytes. Neither age, gender and BMI, nor their combination predicted poorer KL (AUC 0.57), JSN or TOS (AUC 0.59) scores. The inclusion of the TBT parameters in the models improved the global prediction results for KL (AUC 0.69), JSN (AUC 0.73) and TOS (AUC 0.71) scores.

CONCLUSIONS

Several differences were found between the models predictive of three different outcomes (KL, JSN and TOS), indicating different underlying mechanisms. These results suggest that TBT parameters assessed when radiographic signs are not yet apparent on radiographs may be useful in predicting the onset of radiological tibiofemoral OA as well as identifying at-risk patients for future clinical trials.

Anisotropic Discrete Dual-Tree Wavelet Transform for Improved Classification of Trabecular Bone

This paper deals with a new anisotropic discrete dual-tree wavelet transform (ADDTWT) to characterize the anisotropy of bone texture. More specifically, we propose to extend the conventional discrete dual-tree wavelet transform (DDTWT) by using the anisotropic basis functions associated with the hyperbolic wavelet transform instead of isotropic spectrum supports. A texture classification framework is adopted to assess the performance of the proposed transform. The generalized Gaussian distribution is used to model the distribution of the sub-band coefficients. The estimated vector of parameters for each image is then used as input for the support vector machine classifier. Experiments were conducted on synthesized anisotropic fractional Brownian motion fields and on a real database composed of osteoporotic patients and control cases. Results show that the ADDTWT outperforms most of the competing anisotropic transforms with an area under curve rate of 93%.

Subchondral tibial bone texture analysis predicts knee osteoarthritis progression: data from the Osteoarthritis Initiative: Tibial bone texture & knee OA progression

OBJECTIVES

To examine whether trabecular bone texture (TBT) parameters assessed on computed radiographs could predict knee osteoarthritis (OA) progression.

METHODS

This study was performed using data from the Osteoarthritis Initiative (OAI). 1647 knees in 1124 patients had bilateral fixed flexion radiographs acquired 48 months apart. Images were semi-automatically segmented to extract a patchwork of regions of interest (ROI). A fractal texture analysis was performed using different methods. OA progression was defined as an increase in the joint space narrowing (JSN) over 48 months. The predictive ability of TBT was evaluated using logistic regression and receiver operating characteristic (ROC) curve. An optimization method for features selection was used to reduce the size of models and assess the impact of each ROI.

RESULTS

Fractal dimensions (FD's) were predictive of the JSN progression for each method tested with an area under the ROC curve (AUC) up to 0.71. Baseline JSN grade was not correlated with TBT parameters (R < 0.21) but had the same predictive capacity (AUC 0.71). The most predictive model included the clinical covariates (age, gender, body mass index (BMI)), JSN and TBT parameters (AUC 0.77). From a statistical point of view we found higher differences in TBT parameters computed in medial ROI between progressors and non-progressors. However, the integration of TBT results from the whole patchwork including the lateral ROIs in the model provided the best predictive model.

CONCLUSIONS

Our findings indicate that TBT parameters assessed in different locations in the joint provided a good predictive ability to detect knee OA progression.

Fractional Brownian Motion and Rao Geodesic Distance for Bone X-Ray Image Characterization

Osteoporosis diagnosis has attracted particular attention in recent decades. Textured images from the microarchitecture of osteoporotic and healthy subjects show a high degree of similarity, increasing the difficulty of classifying such textures. Thus, the evaluation of osteoporosis from the bone X-ray images presents a major challenge for pattern recognition and medical applications. The purpose of this paper is to use the fractional Brownian motion (fBm) model and the probability density function of its increments to compute a similarity measure with the Rao geodesic distance to classify trabecular bone X-ray images. When evaluated on synthetic fBm images (test vectors) with the well-known Hurst parameter H, the proposed method met our expectations in which a good classification of the synthetic images was achieved. A clinical study was conducted on textured bone X-ray images from two different female populations of osteoporotic patients (fracture cases) and control subjects. Using the proposed method, an area under curve rate of 97% was achieved.

Bone texture characterization for osteoporosis diagnosis using digital radiography

We introduce texture classification techniques to effectively diagnose osteoporosis in bone radiography data. Osteoporosis is an age-related systemic bone skeletal disorder characterized by low bone mass and bone structure deterioriation that results in increased bone fragility and higher fracture risk. Therefore, early diagnosis can effectively predict fracture risk and prevent the disease. Automated diagnosis from digital radiographs is very challenging since the scans of healthy and osteoporotic subjects show little or no visual differences, and their density histograms mostly overlap. We designed a system to separate healthy from osteoporotic subjects using high-dimensional textural feature representations computed from radiographs. These features were then reduced using feature selection to obtain the more discriminant subset that was finally classified by our methods. The top performing approach yields 79.3% accuracy and 81% area under the ROC over 116 bone radiographs.

Directional fractal signature methods for trabecular bone texture in hand radiographs: data from the Osteoarthritis Initiative

PURPOSE

To develop directional fractal signature methods for the analysis of trabecular bone (TB) texture in hand radiographs. Problems associated with the small size of hand bones and the orientation of fingers were addressed.

METHODS

An augmented variance orientation transform (AVOT) and a quadrant rotating grid (QRG) methods were developed. The methods calculate fractal signatures (FSs) in different directions. Unlike other methods they have the search region adjusted according to the size of bone region of interest (ROI) to be analyzed and they produce FSs defined with respect to any chosen reference direction, i.e., they work for arbitrary orientation of fingers. Five parameters at scales ranging from 2 to 14 pixels (depending on image size and method) were derived from rose plots of Hurst coefficients, i.e., FS in dominating roughness (FSSta), vertical (FSV) and horizontal (FSH) directions, aspect ratio (StrS), and direction signatures (StdS), respectively. The accuracy in measuring surface roughness and isotropy/anisotropy was evaluated using 3600 isotropic and 800 anisotropic fractal surface images of sizes between 20 × 20 and 64 × 64 pixels. The isotropic surfaces had FDs ranging from 2.1 to 2.9 in steps of 0.1, and the anisotropic surfaces had two dominating directions of 30° and 120°. The methods were used to find differences in hand TB textures between 20 matched pairs of subjects with (cases: approximate Kellgren-Lawrence (KL) grade ≥ 2) and without (controls: approximate KL grade <2) radiographic hand osteoarthritis (OA). The OA Initiative public database was used and 20 × 20 pixel bone ROIs were selected on 5th distal and middle phalanges. The performance of the AVOT and QRG methods was compared against a variance orientation transform (VOT) method developed earlier [M. Wolski, P. Podsiadlo, and G. W. Stachowiak, "Directional fractal signature analysis of trabecular bone: evaluation of different methods to detect early osteoarthritis in knee radiographs," Proc. Inst. Mech. Eng., Part H 223, 211-236 (2009)].

RESULTS

The AVOT method correctly quantified the isotropic and anisotropic surfaces for all image sizes and scales. Values of FSSta were significantly different (P < 0.05) between the isotropic surfaces. Using the VOT and QRG methods no differences were found at large scales for the isotropic surfaces that are smaller than 64 × 64 and 48 × 48 pixels, respectively, and at some scales for the anisotropic surfaces with size 48 × 48 pixels. Compared to controls, using the AVOT and QRG methods the authors found that OA TB textures were less rough (P < 0.05) in the dominating and horizontal directions (i.e., lower FSSta and FSH), rougher in the vertical direction (i.e., higher FSV) and less anisotropic (i.e., higher StrS) than controls. No differences were found using the VOT method.

CONCLUSIONS

The AVOT method is well suited for the analysis of bone texture in hand radiographs and it could be potentially useful for early detection and prediction of hand OA.

Fracture discrimination by combined bone mineral density (BMD) and microarchitectural texture analysis

The use of bone mineral density (BMD) for fracture discrimination may be improved by considering bone microarchitecture. Texture parameters such as trabecular bone score (TBS) or mean Hurst parameter (H) could help to find women who are at high risk of fracture in the non-osteoporotic group. The purpose of this study was to combine BMD and microarchitectural texture parameters (spine TBS and calcaneus H) for the detection of osteoporotic fractures. Two hundred and fifty five women had a lumbar spine (LS), total hip (TH), and femoral neck (FN) DXA. Additionally, texture analyses were performed with TBS on spine DXA and with H on calcaneus radiographs. Seventy-nine women had prevalent fragility fractures. The association with fracture was evaluated by multivariate logistic regressions. The diagnostic value of each parameter alone and together was evaluated by odds ratios (OR). The area under curve (AUC) of the receiver operating characteristics (ROC) were assessed in models including BMD, H, and TBS. Women were also classified above and under the lowest tertile of H or TBS according to their BMD status. Women with prevalent fracture were older and had lower TBS, H, LS-BMD, and TH-BMD than women without fracture. Age-adjusted ORs were 1.66, 1.70, and 1.93 for LS, FN, and TH-BMD, respectively. Both TBS and H remained significantly associated with fracture after adjustment for age and TH-BMD: OR 2.07 [1.43; 3.05] and 1.47 [1.04; 2.11], respectively. The addition of texture parameters in the multivariate models didn't show a significant improvement of the ROC-AUC. However, women with normal or osteopenic BMD in the lowest range of TBS or H had significantly more fractures than women above the TBS or the H threshold. We have shown the potential interest of texture parameters such as TBS and H in addition to BMD to discriminate patients with or without osteoporotic fractures. However, their clinical added values should be evaluated relative to other risk factors.

A dissimilarity-based multiple classifier system for trabecular bone texture in detection and prediction of progression of knee osteoarthritis

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.

Assessment of bone mineral density and radiographic texture analysis at the tibial subchondral bone

Microstructural changes of subchondral bone constitute one of the figures characterising osteoarthritis on a structural level. Subchondral bone mineral density may reflect the complex relationship between bone and cartilage submitted to movement and loading. In this review, the authors discussed the interest of tibial subchondral bone mineral density assessment in the perspective of its diagnostic, etiopathogenic and prognostic value in osteoarthritis. In addition, the sources of variability linked to the measurement of tibial subchondral bone mineral density are precised. Trabecular bone structure characterisation by radiographic texture analyses may also represent a new promising tool to evaluate the microarchitectural changes that occur with initiation and progression of osteoarthritis. In this paper, the authors also highlighted the interest of different radiographic texture analyses and their clinical relevance in the field of osteoarthritis.

Prediction of progression of radiographic knee osteoarthritis using tibial trabecular bone texture

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

3D shape-dependent thinning method for trabecular bone characterization

PURPOSE

Curve and surface thinning are widely-used skeletonization techniques for modeling objects in three dimensions. In the case of trabecular bone analysis, however, neither curve nor surface thinning is really efficient since the internal geometry of the object is usually composed of both rod and plate shapes. The purpose of this paper is to propose an original method called hybrid skeleton which better matches the geometry of the data compared to curve and surface skeletons. In the hybrid skeleton algorithm, 1D curves represent rod-shaped zones whereas 2D surfaces represent plate-shaped elements.

METHODS

The proposed hybrid skeleton algorithm is based on a combination of three methods. (1) A new variant of the method proposed by Bonnassie et al. for the classification of voxels as belonging to plate-like or rod-like structures, where the medial axis (MA) algorithm is replaced by a fast and connected skeletonization algorithm. In addition, the reversibility of the MA algorithm is replaced by an isotropic region-growth method to spread the rod and plate labels back to the original object. (2) A well chosen surface thinning method applied on the plate voxels set. (3) A well chosen curve skeleton thinning method applied on the rod voxels set. The efficiency and the robustness of the proposed algorithm were evaluated using synthesis test vectors. A clinical study was led on micro-CT (computed tomography) images of two different populations of osteoarthritic and osteoporotic trabecular bone samples. The morphological and topological characteristics of the two populations were evaluated using the proposed hybrid skeleton as well as the classification algorithm.

RESULTS

When evaluated on test vectors and compared to Bonnassie's algorithm, the proposed classification algorithm gives a slightly better rate of classification. The hybrid skeleton preserves the shape information of the processed objects. Interesting morphological and topological features as well as volumetric ones were extracted from the skeleton and from the classified volumes, respectively. The extracted features enable the two populations of osteoarthritic and osteoporotic trabecular bone samples to be distinguished.

CONCLUSIONS

Compared to curve-based or surface-based skeletons, the hybrid skeleton better matches the geometry of the data. Each rod is represented by a one-voxel-thick arc and each plate is represented by a one-voxel-thick surface. The hybrid skeleton as well as the proposed classification algorithm introduce relevant parameters linked to the presence of plates in the trabecular bone data, showing that rods and plates contain independent information about trabeculae. The hybrid skeleton offers a new opportunity for precise studies of porous media such as trabecular bone.

Comparison of radiograph-based texture analysis and bone mineral density with three-dimensional microarchitecture of trabecular bone

PURPOSE

Hip fracture is a serious health problem and textural methods are being developed to assess bone quality. The authors aimed to perform textural analysis at femur on high-resolution digital radiographs compared to three-dimensional (3D) microarchitecture comparatively to bone mineral density.

METHODS

Sixteen cadaveric femurs were imaged with an x-ray device using a C-MOS sensor. One 17 mm square region of interest (ROI) was selected in the femoral head (FH) and one in the great trochanter (GT). Two-dimensional (2D) textural features from the co-occurrence matrices were extracted. Site-matched measurements of bone mineral density were performed. Inside each ROI, a 16 mm diameter core was extracted. Apparent density (Dapp) and bone volume proportion (BV/TV(Arch)) were measured from a defatted bone core using Archimedes' principle. Microcomputed tomography images of the entire length of the core were obtained (Skyscan 1072) at 19.8 microm of resolution and usual 3D morphometric parameters were computed on the binary volume after calibration from BV/TV(Arch). Then, bone surface/bone volume, trabecular thickness, trabecular separation, and trabecular number were obtained by direct methods without model assumption and the structure model index was calculated.

RESULTS

In univariate analysis, the correlation coefficients between 2D textural features and 3D morphological parameters reached 0.83 at the FH and 0.79 at the GT. In multivariate canonical correlation analysis, coefficients of the first component reached 0.95 at the FH and 0.88 at the GT.

CONCLUSIONS

Digital radiographs, widely available and economically viable, are an alternative method for evaluating bone microarchitectural structure.

A signature dissimilarity measure for trabecular bone texture in knee radiographs

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 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.

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.

Radiographic trabecular 2D and 3D parameters of proximal femoral bone cores correlate with each other and with yield stress

Radiographic images of bone cores taken from cadaver proximal femora provided two-dimensional parameters of projected trabecular patterns that correlated highly with conceptually equivalent three-dimensional parameters in the same cores. Measurements also highly correlated with yield stress, suggesting that both parameters have similar biomechanical qualities.

INTRODUCTION

We compared morphometric measurements of trabecular patterns in two-dimensional (2D) projection radiographic images of cores from cadaver proximal femoral bones with conceptually equivalent measurements from three-dimensional microcomputed tomography (3D microCT) images.

METHODS

Seven cadaver proximal femora provided 47 excised cores from seven regions. Digitized radiographs of those cores were processed with software that extracts trabecular patterns. Measurements of their distribution, geometry, and connectivity were compared with 3D parameters of similar definition derived from microCT of those cores. The relationship between 2D and 3D measurements and yield stress was also examined.

RESULTS

2D measurements strongly correlated with conceptually equivalent measurements obtained using 3D microCT. In all cases, the correlation coefficients were high, ranging from r = 0.84 (p < 0.001) to r = 0.93 (p < 0.001). The correlation coefficients between 2D and 3D measurements and yield stress of the cores were also high (r = 0.60 and 0.82, p < 0.001, respectively).

CONCLUSIONS

These findings provide correlative and biomechanical evidence supporting the qualitative similarity of 2D microstructural parameters extracted from plain proximal femoral core X-ray images to conceptually equivalent 3D microstructural measurements of those same cores.

Alveolar bone changes under overhanging restorations

The aim of this study was to investigate changes in the trabecular architecture of the alveolar bone beneath overhanging restorations with bitewing radiographs in patients having no radiographically visible vertical bone loss. Twenty-eight digital bitewing radiographs with overhanging restorations and 28 digital bitewing radiographs without any restorations belonging to the contralateral side of the same patient were included in the study. Regions of interests (ROI) were created in the alveolar bone along the interproximal regions. These ROIs were segmented to binary images with ImageJ, and, within these binary images, the number of radiographically visible trabecular bone islands per unit area was counted; in addition, the Feret diameter and fractal dimension (FD) were measured. It was found that the mean number of objects per unit area was statistically different in alveolar bone with overhanging restorations from control sites (p < 0.0001). However, the FeD (p = 0.179) and FD (p = 0.963) did not show statistically significant differences between alveolar bone with and without overhanging restorations.

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

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.

Comparable ecological dynamics underlie early cancer invasion and species dispersal, involving self-organizing processes

Occupancy of new habitats through dispersion is a central process in nature. In particular, long-distance dispersal is involved in the spread of species and epidemics, although it has not been previously related with cancer invasion, a process that involves cell spreading to tissues far away from the primary tumour. Using simulations and real data we show that the early spread of cancer cells is similar to the species individuals spread and we suggest that both processes are represented by a common spatio-temporal signature of long-distance dispersal and subsequent local proliferation. This signature is characterized by a particular fractal geometry of the boundaries of patches generated, and a power-law scaled, disrupted patch size distribution. In contrast, invasions involving only dispersal but not subsequent proliferation ("physiological invasions") like trophoblast cells invasion during normal human placentation did not show the patch size power-law pattern. Our results are consistent under different temporal and spatial scales, and under different resolution levels of analysis. We conclude that the scaling properties are a hallmark and a direct result of long-distance dispersal and proliferation, and that they could reflect homologous ecological processes of population self-organization during cancer and species spread. Our results are significant for the detection of processes involving long-range dispersal and proliferation like cancer local invasion and metastasis, biological invasions and epidemics, and for the formulation of new cancer therapeutical approaches.

Clinical interest of bone texture analysis in osteoporosis: a case control multicenter study

We demonstrate the clinical interest of bone texture analysis with a new high resolution X-ray device. We have found that the combination of BMD and texture parameter values provided a better assessment of the fracture risk than that obtainable solely by BMD measurement.

INTRODUCTION

Osteoporosis is characterized by BMD and trabecular bone microarchitecture. We have developed a new high-resolution X-ray device with direct digitization. The aim of this study was to demonstrate in a multicenter case control study the clinical interest of bone texture analysis with this new device.

METHODS

In this cross-sectional multicenter case-control population study in post-menopausal women, 159 osteoporotic fractures were compared with 219 control cases. Images were obtained on calcaneus with a direct digital X-ray device (BMA, D3A Medical Systems). Co-occurrence, run-length matrices and the fractal parameter Hmean were evaluated. BMD was measured at the lumbar spine (LS), femoral neck (FN) and total hip (TH) by DXA.

RESULTS

The three texture parameters were significantly lower in osteoporotic fracture cases than in control cases. These differences persisted after adjustment for TH BMD. Receiver operating characteristic curves were used to compare the discriminant capacity of texture parameters and BMD measurements for fracture. The highest areas under curve (AUC) were 0.721 for TH BMD and 0.706 for Hmean (AUC THBMD vs. AUC Hmean, p = NS). We determined the threshold between high and low Hmean parameter values and then the odds ratios (OR) of fracture for low Hmean, for BMD < or =2.5 SD in the T-score and for combinations of both parameters. The OR of fracture for low H was 2.72 (95% CI, 1.36-5.4). For a FN BMD < or = -2.5 SD, the OR of 4.78 (2.19-10.43) shifted to 14.06 (4.41-44.85) adding H.

CONCLUSIONS

These data confirmed the clinical interest of the combination of BMD and texture parameters to improve the assessment of the risk of fracture other that obtainable by the sole BMD measurement.

Differences in trabecular bone texture between knees with and without radiographic osteoarthritis detected by fractal methods

OBJECTIVE

To develop an accurate method for quantifying differences in the trabecular structure in the tibial bone between subjects with and without knee osteoarthritis (OA).

METHODS

Standard knee radiographs were taken from 26 subjects (seven women) with meniscectomy and radiographic OA Kellgren & Lawrence grade 2 or worse in the medial compartment. Each case knee was individually matched by sex, age, body mass index and medial or lateral compartment with a control knee. A newly developed augmented Hurst orientation transform (HOT) method was used to calculate texture parameters for regions selected in X-ray images of non-OA and OA tibial bones. This method produces a mean value of fractal dimensions (FD MEAN), FDs in the vertical (FDV) and horizontal (FDH) directions and along a direction of the roughest part of the tibial bone (FDSta), fractal signatures and a texture aspect ratio (Str). The ratio determines a degree of the bone texture anisotropy. Reproducibility was calculated using an intraclass correlation coefficient (ICC). Comparisons between cases and controls were made with paired t tests. The performance of the HOT method was evaluated against a benchmark fractal signature analysis (FSA) method.

RESULTS

Compared with controls, trabecular bone in OA knees showed significantly lower FD MEAN, FDV, FDH and FDSta and higher Str at trabecular image sizes 0.2-1.1mm (P<0.05, HOT). The reproducibility of all parameters was very good (ICC>0.8). In the medial compartment, fractal signatures calculated for OA horizontal and vertical trabeculae were significantly lower at sizes 0.3-0.55 mm (P<0.05, HOT) and 0.3-0.65 mm (P<0.001, FSA). In the lateral compartment, FDs calculated for OA trabeculae were lower than controls (horizontal: 0.3-0.55 mm (P<0.05, HOT) and 0.3-0.65 mm (P<0.001, FSA); vertical: 0.3-0.4mm (P<0.05, HOT) and 0.3-0.35 mm (P<0.001, FSA).

CONCLUSION

The augmented HOT method produces fractal signatures that are comparable to those obtained from the benchmark FSA method. The HOT method provides a more detailed description of OA changes in bone anisotropy than the FSA method. This includes a degree of bone anisotropy measured using data from all possible directions and a texture roughness calculated for the roughest part of the bone. It appears that the augmented HOT method is well suited to quantify OA changes in the tibial bone structure.