Changyong Guo

Automatic segmentation technique for acetabulum and femoral head in CT images

Abstract Segmentation of the femoral head and proximal acetabulum from three dimensional (3D) CT data is essential for patient specific planning and simulation of hip surgery whereas it still remains challenging due to deformed shapes and extremely narrow inter-bone regions. In this paper, we present an accurate, automatic and fast approach for simultaneous segmentation of the femoral head and proximal acetabulum in the hip joint from 3D CT data. First valley-emphasized image is constructed from original images so that valleys stand out in high relief and initial thresholding segmentation is performed to divide the image set into bone (femoral head and acetabulum) and non-bone classes. It is employed as an initial boundary of the femoral head and acetabulum for further processing in the segmentation procedures. In the subsequent iterative process, the bone regions are further segmented with consideration of the narrow joint space, the neighborhood information and the partial volume effect. Finally, the segmented bone boundaries are corrected based on the normal direction of vertices of the 3D bone surface. Evaluation of the method is performed on the 110 hips including pathologies. Experimental results indicate that our method rapidly leads to very accurate segmentations of the femoral head and acetabulum in the hip joint and can be applied as a tool in the clinical practice.

Accuracy Limits for the Thickness Measurement of the Hip Joint Cartilage in 3-D MR Images: Simulation and Validation

This paper describes a theoretical simulation method for ascertaining the inherent limits on the accuracy of thickness measurement of hip joint cartilage in 3-D MR images. This method can specify where and how thickness can be measured with sufficient accuracy under the certain MR imaging conditions. In the numerical simulation, we present a mathematical model for two adjacent sheet structures separated by a small distance, which simulated the femoral and acetabular cartilage and the joint space width in the hip joint; moreover, we perform the numerical simulation of MR imaging and postprocessing for thickness measurement. We especially focused on the effects of voxel anisotropy in MR imaging with variable orientation of cartilage surface and different joint space width. Also, thickness measurement is performed in MR imaging with isotropic voxel. The results from MR data with isotropic voxels show that accurate measurement of cartilage thickness at location of measured values of the hip joint space width and the cartilage thickness being two times as large as the voxel size or above should be possible. The simulation method is validated by comparison with the actual results obtained from the experiments using three phantoms, five normal cadaver hip specimens, and nine patients with osteoarthritis.

An analysis algorithm for accurate determination of articular cartilage thickness of hip joint from MR images

To test the accuracy of the most widely used technique based on edge detection for thickness measurement of the hip joint cartilage in MR images, and to improve the measurement accuracy by developing a new measurement method based on a model of the MRI process.

A technique for visualization and mapping of local cartilage thickness changes in MR images of osteoarthritic knee.

PURPOSE The aim of this paper is to describe a technique for the visualization and mapping of focal, local cartilage thickness changes over time in magnetic resonance images of osteoarthritic knee. METHODS Magnetic resonance imaging was performed in 25 fresh frozen pig knee joints and 15 knees of patients with borderline to mild osteoarthritis (51.2±6.3 years). Cartilage and corresponding bone structures were extracted by semi-automatic segmentation. Each point in the bone surface which was part of the bone-cartilage interface was assigned a cartilage thickness value. Cartilage thicknesses were computed for each point in the bone-cartilage interfaces and transferred to the bone surfaces. Moreover, we developed a three dimensional registration method for the identification of anatomically corresponding points of the bone surface to quantify local cartilage thickness changes. One of the main advantages of our method compared to other studies in the field of registration is a global optimization algorithm that does not require any initialization. RESULTS AND CONCLUSION The registration accuracy was 0.93±0.05 mm (less than a voxel of magnetic resonance data). Local cartilage thickness changes were seen as having follow-up clinical study for detecting local changes in cartilage thickness. Experiment results suggest that our method was sufficiently accurate and effective for monitoring knee joint diseases.

Surface-based rigid registration using a global optimization algorithm for assessment of MRI knee cartilage thickness changes

Abstract Registration methods have become an important tool in many medical applications. Existing methods require a good initial estimation (transformation) in order to find a global solution, i.e., if the initial estimation is far from the actual solution, incorrect solution or mismatching is very likely. In contrast, this paper presents a novel approach for globally solving the three dimensional (3D) rigid registration problem. The registration is grounded on a mathematical theory—Lipschitz optimization. It achieves a guaranteed global optimality with a rough initial estimation (e.g., even a random guess). Moreover, Munkres assignment algorithm is used to find the point correspondences. It applies the distance matrix to find an optimal correspondence. Our method is evaluated and demonstrated on MR images from porcine knees and human knees. Compared with state-of-the-art methods, the proposed technique is more robust, more accurate to perform point to point comparisons of knee cartilage thickness values for follow-up studies on the same subject.

Segmentation of the hip joint in CT volumes using adaptive thresholding classification and normal direction correction

Segmentation of the pelvis and proximal femur in computed tomography (CT) volumes is a prerequisite of patient specific planning and simulation for hip surgery. Existing methods do not perform well due to bone disease and technical limitations of CT imaging. In this paper, an accurate framework for segmenting bone in the hip joint is presented. Our approach begins with valley-emphasized image construction using morphological operations so that valleys stand out in high relief, and then, an initial segmentation with optimal threshold is performed to divide the dataset into bone and non-bone regions. Subsequently, bone regions are reclassified based on 3D iterative adaptive thresholding with consideration of the partial volume effect and the spatial information. Finally, we refine the rough bone boundaries based on the normal direction of vertices of the 3D bone surface. Our segmentation approach is automatic and robust. Its performance is evaluated on 35 datasets consisting of 70 hip joints with a status ranging from healthy to severe osteoarthritis and the results have proved to be very successful.

Accurate three-dimensional registration of magnetic resonance images for detecting local changes in cartilage thickness

The purpose of this study is to develop a three-dimensional registration method for monitoring knee joint disease from magnetic resonance (MR) image data sets. A global optimization technique was used for identifying anatomically corresponding points of knee femur surfaces (bone cartilage interfaces). In a first pre-registration step, we used the principal axes transformation to correct for different knee joint positions and orientations in the MR scanner. In a second step, we presented a global search algorithm based on Lipschitz optimization theory. This technique can simultaneously determine the translation and rotation parameters through searching a six-dimensional space of Euclidean motion metrics (translation and rotation) after calculating the point correspondences. The point correspondences were calculated by using the Hungarian algorithm. The accuracy of registration was evaluated using 20 porcine knees. There were 300 corresponding landmark points over the 20 pig knees. We evaluated the registration accuracy by measuring the root-mean-square distance (RMSD) error of corresponding landmark points between two femur surfaces (two time-points). The results show that the average RMSD was 1.22 ± 0.10 mm (SD) by the iterative closest point (ICP) method, 1.17 ± 0.10 mm the by expectation-maximization-ICP method, 1.02 ± 0.06 mm by the genetic method, and 0.93 ± 0.04 mm by the proposed method. Compared with the other three registration approaches, the proposed method achieved the highest registration accuracy.

Accurate bone registration in knee MR images

Changes of the cartilage morphology over time can tell the progression of osteoarthritis (OA) and show particular promise for evaluating the efficacy of disease-modifying OA drugs. Hence, cartilage matching is required prior to cartilage morphology comparison. An accurate cartilage matching allows one to ensure longitudinal focal and local changes of cartilage morphology due to OA. The method described in this article meets this need. The proposed method consists of three steps. First, the knee femur surfaces are aligned, using the principal axes transformation to correct for different knee joint positions and orientations in the magnetic resonance (MR) scanner. Second, we present a global registration algorithm based on Lipschitz optimization theory for accurately identifying the corresponding points of the knee femur surface. Third, the rigid transformation of the knee femur surface registration is applied to the cartilage surface. Our registration algorithm is efficient and robust, and its performance is evaluated on MR images of pig knees.

Point to Point Registration Based on MRI sequences

The purpose of this study is to develop a new method for the registration of 3D cartilage plates in the MR data set. This technique traces local cartilage thickness changes over time. In a first rough registration step, the principal axes transformation (PAX) and landmark methods are used. In the second step, an improved iterative closest point (ICP) method is used. The point sets from segmentation of MRI sequences are registratered in these processes. The experimental results demonstrate the effectiveness of this method for the registration of the MRI of knee cartilage over time based on the cartilage and bone interfaces.

Calibration of Thickness Measurement Errors of Hip Joint Cartilage from MR Images

In the hip joint, the femoral and acetabular cartilages are in proximity to each other, a conventional measurement technique based on the edge detection, can introduce large underestimation errors in measurement of cartilage thickness. In this study, we develop a model-based approach for accurate thickness measurement. We model the imaging process of two adjacent sheet structures, which simulate two articular cartilages in the hip joint. This model can be used to predict the shape of the intensity profile along the sheet normal orientation. Using an optimization technique, the model parameters are adjusted to minimize the difference between the predicted intensity profile and the actual intensity profiles observed in the MR data. The set of model parameters that minimize the difference between the model and the MR data yield the thickness estimation. Using three cadaveric human hip joints, we present results showing that the new model-based approach is more accurate than the edge detection method at estimating the hip cartilage thickness.