Knee osteoarthritis (KOA) is a prevalent musculoskeletal disorder, often diagnosed using X-rays due to its cost-effectiveness. While Magnetic Resonance Imaging (MRI) provides superior soft tissue visualization and serves as a valuable supplementary diagnostic tool, its high cost and limited accessibility significantly restrict its widespread use. To explore the feasibility of bridging this imaging gap, we conducted a feasibility study leveraging a diffusion-based model that uses an X-ray image as conditional input, alongside target depth and additional patient-specific feature information, to generate corresponding MRI sequences. Our findings demonstrate that the MRI volumes generated by our approach are not only visually closer to real MRI scans compared with other methods but also achieve the highest quantitative performance in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM). Furthermore, by increasing the number of inference steps to interpolate between slice depths, we enhance the continuity of the generated volume, achieving higher adjacent slice correlation coefficients. Through ablation studies, we further validate that integrating supplemental patient-specific information, beyond what X-rays alone can provide, enhances the accuracy and clinical relevance of the generated MRI, which underscores the potential of leveraging external patient-specific information to improve the performance of the MRI generation.

In this paper, we introduce a benchmark dataset for the diagnosis and treatment planning of Class III malocclusion, a condition that requires precise evaluation to determine the necessity of surgical intervention. Our dataset comprises paired lateral cephalometric X-rays and profile photographs, each annotated with a treatment plan mainly indicating whether surgery is required or not. We assess state-of-the-art deep learning models on both imaging modalities to explore their potential for automating diagnosis and treatment decisions. Notably, the dataset facilitates research into non-radiographic diagnostic approaches, potentially enabling treatment planning based solely on profile photographs. We release this dataset as a resource to advance automated applications in orthodontics and maxillofacial surgery.

Knee osteoarthritis (OA) is a prevalent and debilitating condition, significantly impacting quality of life and mobility. Traditional 3D image classification methods often falter in effectively diagnosing this complex condition, primarily due to their inability to capture and analyze the discrete, informative nuances inherent in individual MRI scan slices. To overcome this limitation, we present a pioneering deep learning framework leveraging Multi-Instance Learning (MIL) to enhance knee OA detection from 3D MRI scans. This novel approach treats each MRI slice as an independent instance, harnessing unique pathological details that contribute collectively to a more accurate and comprehensive diagnosis. Evaluated on an extensive dataset of$\mathbf{9 0 0}$patients from the public OAI database, our MIL-based method demonstrates superior diagnostic performance, marking a significant leap over conventional imaging techniques, achieving an AUC of$\mathbf{9 3. 4 1 \%}$.

Knee osteoarthritis (KOA) is a widespread condition that can cause chronic pain and stiffness in the knee joint. Early detection and diagnosis are crucial for successful clinical intervention and management to prevent severe complications, such as loss of mobility. In this paper, we propose an automated approach that employs the Swin Transformer to predict the severity of KOA. Our model uses publicly available radiographic datasets with Kellgren and Lawrence scores to enable early detection and severity assessment. To improve the accuracy of our model, we employ a multi-prediction head architecture that utilizes multi-layer perceptron classifiers. Additionally, we introduce a novel training approach that reduces the data drift between multiple datasets to ensure the generalization ability of the model. The results of our experiments demonstrate the effectiveness and feasibility of our approach in predicting KOA severity accurately.

Knee OsteoArthritis (KOA) is a common musculoskeletal disorder, which causes reduced mobility for seniors. Early detection of such a disorder is important to limit its impact on people. Computer-Aided Diagnosis (CAD) systems based on deep learning methods have shown success in KOA diagnosis. Due to the high cost of labelling, the lack of sufficient data in the medical field is a significant challenge for training machine learning models. To improve the generalization capability of deep neural network models and avoid overfitting, data augmentation is essential. However, existing data augmentation techniques such as rotation and gamma correction are not effective at increasing the diversity of the original data. In this paper, we propose a novel approach based on the Vision Transformer (ViT) model with a Selective Shuffled Position Embedding (SSPE) strategy that generates different input sequences fixing and shuffling the position embedding of key and non-key patches, respectively, as a novel method of data augmentation for early detection of KOA (KL-0 vs KL-2). Experimental results demonstrate that our approach is valid as it can significantly improve the model’s classification performance.

Early diagnosis of osteoporosis can efficiently predict fracture risk. There is a great demand to prevent this disease. The goal of this study was to distinguish osteoporotic cases from healthy controls on 2D bone radiograph images, using texture analysis and genetic algorithms (GAs). Gray Level Co-occurrence Matrix (GLCM), Run length Matrix (RLM) and Binarized Statistical Image Features (BSIF) were used for texture analysis. Features are numerous and parameter-dependent. The related experts can pick out the useful input features for the classifier. It however remains a difficult task and may be inefficient or even harmful as the data pattern is not clear. In this paper, GAs were used to optimize the two parameters of the co-occurrence matrix (distance parameter or pixel separation, orientation or direction) and the number of gray levels used in the preprocessing quantification step. GAs were also used to select the best combination of features extracted from GLCM and RLM matrices. Experiments were conducted on two populations composed of Osteoporotic Patients and Control Subjects. Results show that GAs combined with GLCM and BSIF features can improve the classification rates (ACC = 87.50%) obtained using GLCM (ACC = 77.8%) alone.

OsteoArthritis (OA) is the most common disorder of the musculoskeletal system and the major cause of reduced mobility among seniors. The visual evaluation of OA still suffers from subjectivity. Recently, Computer-Aided Diagnosis (CAD) systems based on learning methods showed potential for improving knee OA diagnostic accuracy. However, learning discriminative properties can be a challenging task, particularly when dealing with complex data such as X-ray images, typically used for knee OA diagnosis. In this paper, we introduce a Discriminative Regularized Auto Encoder (DRAE) that allows to learn both relevant and discriminative properties that improve the classification performance. More specifically, a penalty term, called discriminative loss is combined with the standard Auto-Encoder training criterion. This additional term aims to force the learned representation to contain discriminative information. Our experimental results on data from the public multicenter OsteoArthritis Initiative (OAI) show that the developed method presents potential results for early knee OA detection.

This paper presents texture characterization techniques for effective diagnosis of osteoporosis cases on bone radiograph images. The automatic classification of osteoporosis and healthy (control) cases with bone radiograph images presents a major challenge as the images show little or no visual difference for both cases. The proposed work uses multifractal analysis to characterize the texture of bone radiographs. A regularization dimension method is newly introduced in this work for texture analysis of bone radiographs. Series of local fractal dimensions are used here instead of single global fractal dimension to characterize local texture variations effectively. The performance of the regularization dimension method is compared to the performance of the box-counting method. Then, regularization dimension and box-counting dimension features are combined to improve the performance. The proposed method is evaluated using texture characterization of bone radiograph (TCB) challenge dataset and compared against other methods participated in the challenge to demonstrate the effectiveness of the proposed method

In this study, the authors introduce new solutions and improvements to the multi‐channel blind image deconvolution problem. More precisely, authors’ contributions are threefold: (i) At first, a simplified version of the existing cross‐relation method for blind system identification is proposed; but most importantly, the authors incorporate into the channel estimation cost function a sparsity constraint to deal with the challenging issue of channel order overestimation errors; (ii) then, once the channel identification is achieved, a new image restoration method based on the stack decoding algorithm is introduced; and (iii) finally, a refining approach using an ‘all‐at‐once’ optimisation technique with an improved mixed norm regularisation is considered. The performance of the proposed approach was evaluated using several numerical simulations. Blind system identification and image restoration tasks were evaluated with respect to several criteria: numerical complexity, robustness to noise effects and channel order estimation. The results obtained are promising and highlight the effectiveness of the proposed approach.

Osteoporosis is one of the most common bone diseases over the world, this disease causes a high risk of fracture, an early diagnosis of the osteoporosis can help avoiding the risk of fracture. Several methods have been proposed to detect the presence of this disease. In this paper we propose a method of osteoporosis diagnosis based on bone X-Ray images, using the frequency separation and fractional Brownian motion. The classification is performed using the Support Vector Machines classifier. The proposed method shows its good classification performance achieving an accuracy rate around 94 %.

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.

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%.

The importance of phase features for texture analysis has been earlier established for many image processing applications. However, the modeling of the phase data faces some difficulties as its information gathers data with rotating values and thus highly sensitive to distortions. Motivated by its ability to capture different shapes of histograms, in this communication, we propose the Gaussian Mixture Model (GMM) to characterize the behavior of relative phase. The Maximum-likelihood Estimator (MLE) is used to estimate the GMM parameters. To investigate the relevance of the GMM model for relative phase data, a feature vector incorporating the estimated parameters is proposed for a multiclass classification task. Experiments are conducted on textures from VisTex and Brodatz databases. Results demonstrate that the GMM model fit well relative phase data. In addition, higher rates of accuracy, precision and recall, 95.35%, 95.50% and 95.40%, respectively, were achieved for Brodatz textures using the proposed feature vector. This suggests the potential usefulness of the probabilistic proprieties for texture analysis.

High Resolution peripheral Quantitative Computed Tomography (HR-pQCT) imaging for studying bone disease has become increasingly common. However, due to the bone inhomogeneity and the noise characteristics, segmentation of HR-pQCT data remains a challenging task. In this work we propose a novel segmentation technique of the cortical bone based on fuzzy energy active contours model. A novel approach as well as a new formulation of the fuzzy membership function are proposed to deal with the HR-pQCT inhomogeneity and separate the cortical bone from the trabecular one. Results show the efficiency and the high accuracy of the proposed approach compared to different existing techniques in terms of Dice similarity coefficient (DSC). Proposed method provides high result (DSC: 90.14±1.64%) compared to Burghardt (DSC: 85.86±3,16%) and FEBAC (DSC: 85.5±4.52%).

The segmentation of the bone in HR-pQCT (High Resolution peripheral Quantitative Computed Tomography) images remains a challenging task due to the image characteristics and the complex structure of the bone (cortical and trabecular). In this paper, we address the problem of separating the cortical bone from the background and the trabecular bone. We propose a novel approach to segment the cortical bone using dual active contours. This new concept allows the two contours to interact with each other and evolve to delineate the cortical bone. The energy of the two contours is based on the local information along the curve to be able to handle the intensity inhomogeneity, the noise characteristics and the motion artefacts in such images. Unlike state of art methods, the proposed technique can segment accurately the cortical bone in the different sites (proximal and ultra-distal). Finally, to test the robustness and accuracy of our proposed method, we compared the computed segmentation results of each method to the ground truth. Our proposed approach gives a higher Dice similarity coefficient in both proximal and ultra-distal sites.

In this paper, we present a new method for modeling disordered porous media. Recent work has shown that the line skeleton is a powerful tool for 3D structure characterization. However, as it generates only 1D curves, the geometry of the material is sometimes excessively approximated. Our algorithm is an improved solution as it considers the local shape of each element that composes the structure of the medium. It consists in an efficient combination of curve and surface thinning techniques. Features extracted from the new skeleton contain significant topological and morphological information. A clinical study carried out on trabecular bone samples demonstrates the ability of our method to discriminate between 2 different populations.

In this letter, it is shown that fast and exact fractional Brownian motion (fBm) and fractional Gaussian noise (fGn) signals can be synthesized by the circulant embedding method (CEM). CEM consists in embedding the N/spl times/N covariance matrix of the stationary fGn process in a larger 2M/spl times/2M circulant matrix such that M /spl ges/N-1. CEM is exact, since second-order statistics of the generated data are those of the Gaussian fGn. CEM is fast, since the optimal case M=N-1 can be reached. Fast and exact fBm sequences can be easily recovered from fGn ones.

Fractal analysis of bone X-ray images has received much interest recently for the diagnosis of bone disease. In this paper, we propose a fractal analysis of bone X-ray tomographic microscopy (XTM) projections. The aim of the study is to establish whether or not there is a correlation between three-dimensional (3-D) trabecular changes and two-dimensional (2-D) fractal descriptors. Using a highly collimated beam, 3-D bone X-ray tomographic images were obtained. Trabecular bone loss was simulated using a mathematical morphology method. Then, 2-D projections were generated in each of the three orthogonal directions. Finally, the model of fractional Brownian motion (fBm) was used on bone XTM 2-D projections to characterize changes in bone structure that occur during disease, such a simulation of bone loss. Results indicate that fBm is a robust texture model allowing quantification of simulations of trabecular bone changes.