Extraction Of Bone Contours Research Articles

Robust x-ray image segmentation by spectral clustering and active shape model.

Extraction of bone contours from x-ray radiographs plays an important role in joint space width assessment, preoperative planning, and kinematics analysis. We present a robust segmentation method to accurately extract the distal femur and proximal tibia in knee radiographs of varying image quality. A spectral clustering method based on the eigensolution of an affinity matrix is utilized for x-ray image denoising. An active shape model-based segmentation method is employed for robust and accurate segmentation of the denoised x-ray images. The performance of the proposed method is evaluated with x-ray images from the public-use dataset(s), the osteoarthritis initiative, achieving a root mean square error of [Formula: see text] for femur and [Formula: see text] for tibia. The results demonstrate that this method outperforms previous segmentation methods in capturing anatomical shape variations, accounting for image quality differences and guiding accurate segmentation.

Head bone tissue extraction algorithm based on CTA image

Vascular tissue and bone tissue cannot be clearly separated based solely on grayscale information in images of computed tomography angiography( CTA). The algorithm based on the growth of threedimensional( 3D) region improved bone tissue outside the bone contour extraction and the extraction algorithm based on improved Snake model combined with the characteristics of CTA grayscale images were proposed.Combining the knowledge of probability theory to improve the accuracy of determining condition of the region growing,fast skeletal regional seed extraction method of 3D region growing was proposed. It made it possible to obtain more accurate bone tissue area. After the Snake model was selected and some improvements were made to the model,energy image information items were increased,so that the model can better solve the current problems. Finally,the experimental results were given and compared with results from the traditional algorithm. It is confirmed the proposed segmentation of bone tissue extraction algorithm works well.

Fully Automatic Segmentation of the Proximal Femur Using Random Forest Regression Voting

Extraction of bone contours from radiographs plays an important role in disease diagnosis, preoperative planning, and treatment analysis. We present a fully automatic method to accurately segment the proximal femur in anteroposterior pelvic radiographs. A number of candidate positions are produced by a global search with a detector. Each is then refined using a statistical shape model together with local detectors for each model point. Both global and local models use Random Forest regression to vote for the optimal positions, leading to robust and accurate results. The performance of the system is evaluated using a set of 839 images of mixed quality. We show that the local search significantly outperforms a range of alternative matching techniques, and that the fully automated system is able to achieve a mean point-to-curve error of less than 0.9 mm for 99% of all 839 images. To the best of our knowledge, this is the most accurate automatic method for segmenting the proximal femur in radiographs yet reported.

A Comparison of X-Ray Image Segmentation Techniques

1 Abstract—Image segmentation operation has a great importance in most medical imaging applications, by extracting anatomical structures from medical images. There are many image segmentation techniques available in the literature, each of them having advantages and disadvantages. The extraction of bone contours from X-ray images has received a considerable amount of attention in the literature recently, because they represent a vital step in the computer analysis of this kind of images. The aim of X-ray segmentation is to subdivide the image in various portions, so that it can help doctors during the study of the bone structure, for the detection of fractures in bones, or for planning the treatment before surgery. The goal of this paper is to review the most important image segmentation methods starting from a data base composed by real X-ray images. We will discuss the principle and the mathematical model for each method, highlighting the strengths and weaknesses.

Automatic extraction of proximal femur contours from calibrated X‐ray images using 3D statistical models: an in vitro study

Accurate extraction of bone contours from two-dimensional (2D) projective X-ray images is an important component for computer-assisted diagnosis, planning or three-dimensional (3D) reconstruction. We propose a 3D statistical model-based, fully automatic segmentation framework for extracting the proximal femur contours from calibrated X-ray images. The automatic initialization is an estimation of a Bayesian network algorithm to fit a multiple-component geometrical model to the X-ray data. The contour extraction is accomplished by a non-rigid 2D/3D registration between the statistical model and the X-ray images, in which bone contours are extracted by a graphical model-based Bayesian inference. The contour extraction algorithm was verified on both cadaver and clinical datasets, visually and quantitatively. Compared to the 'gold standard', a mean error of 1.6 mm was observed when the automatically extracted contours were used to reconstruct a patient-specific surface model. Our statistical model-based bone contour extraction approach holds the potential to facilitate the application of 2D/3D reconstruction in surgical navigation.

Automatic extraction of proximal femur contours from calibrated X-ray images: a Bayesian inference approach

Automatic identification and extraction of bone contours from X-ray images is an essential first step task for further medical image analysis. This paper proposed a 3D-statistical-model-based framework for the proximal femur bone contour extraction from calibrated X-ray images. The initialisation to align the statistical model is solved by a particle filter on a dynamic Bayesian network to fit a multiple component geometrical model to the X-ray images. The contour extraction is accomplished by a non-rigid 2D?3D registration between the X-ray images and the statistical model, in which bone contours are extracted by a graphical-model-based Bayesian inference. Experiments on clinical data set verified its robustness against occlusion.

Automatic Extraction of Femur Contours from Calibrated X-Ray Images using Statistical Information

Automatic identification and extraction of bone contours from x-ray images is an essential first step task for further medical image analysis. In this paper we propose a 3D statistical model based framework for the proximal femur contour extraction from calibrated x-ray images. The automatic initialization to align the 3D model with the x-ray images is solved by an Estimation of Bayesian Network Algorithm to fit a simplified multiple component geometrical model of the proximal femur to the x-ray data. Landmarks can be extracted from the geometrical model for the initialization of the 3D statistical model. The contour extraction is then accomplished by a joint registration and segmentation procedure. We iteratively updates the extracted bone contours and an instanced 3D model to fit the x-ray images. Taking the projected silhouettes of the instanced 3D model on the registered x-ray images as templates, bone contours can be extracted by a graphical model based Bayesian inference. The 3D model can then be updated by a non-rigid 2D/3D registration between the 3D statistical model and the extracted bone contours. Preliminary experiments on clinical data sets verified its validity.

Development and validation of a generic 3D model of the distal femur

The development and validation of a virtual generic 3D model of the distal femur using computer graphical methods is presented. The synthesis of the generic model requires the following steps: acquisition of bony 3D morphology using standard computed tomography (CT) imaging; alignment of 3D models reconstructed from CT images with a common coordinate system; computer graphical sectioning of the models; extraction of bone contours from the image sections; combining and averaging of extracted contours; and 3D reconstruction of the averaged contours. The generic models reconstructed from the averaged contours of six cadaver femora were validated by comparing their surface geometry on a point to point basis with that of the CT reconstructed reference models. The mean errors ranged from 0.99 to 2.5 mm and were in agreement with the qualitative assessment of the models.

Border-tracing algorithm implementation for the femoral geometry reconstruction

In some orthopaedic applications such as the design of custom-made hip prostheses, reconstruction of the bone morphology is a fundamental step. Different methods are available to extract the geometry of the femoral medullary canal from computed tomography (CT) images. In this research, an automatic procedure (border-tracing method) for the extraction of bone contours was implemented and validated. A composite replica of the human femur was scanned and the CT images processed using three different methods, a manual procedure; the border-tracing algorithm; and a threshold-based method. The resulting contours were used to estimate the accuracy of the implemented procedure. The two software techniques were more accurate than the manual procedure. Then, these two procedures were applied to an in vivo CT data set in order to determine to most critical region for repeatability. Only for the images located in this region, the repeatability measurement was carried out for six in vivo CT data sets to evaluate the inter-femur repeatability. The border-tracing method was found to achieve the highest repeatability.