[Show abstract][Hide abstract] ABSTRACT: Articular cartilage is a solid-fluid biphasic material covering the bony ends of articulating joints. Hydration of articular cartilage is important to joint lubrication and weight-wearing. The aims of this study are to measure the altered hydration behaviour of the proteoglycan-degraded articular cartilage using high-frequency ultrasound and then to investigate the effect of proteoglycan (PG) degradation on cartilage hydration.
Twelve porcine patellae with smooth cartilage surface were prepared and evenly divided into two groups: normal group without any enzyme treatment and trypsin group treated with 0.25% trypsin solution for 4h to digest PG in the tissue. After 40-minute exposure to air at room temperature, the specimens were immerged into the physiological saline solution. The dehydration induced hydration behaviour of the specimen was monitored by the high-frequency (25 MHz) ultrasound pulser/receiver (P/R) system. Dynamic strain and equilibrium strain were extracted to quantitatively evaluate the hydration behaviour of the dehydrated cartilage tissues.
The hydration progress of the dehydrated cartilage tissue was observed in M-mode ultrasound image indicating that the hydration behaviour of the PG-degraded specimens decreased. The percentage value of the equilibrium strain (1.84 +/- 0.21 %) of the PG-degraded cartilage significantly (p < 0.01) decreased in comparison with healthy cartilage (3.46 +/- 0.49 %). The histological sections demonstrated that almost PG content in the entire cartilage layer was digested by trypsin.
Using high-frequency ultrasound, this study found a reduction in the hydration behaviour of the PG-degraded cartilage. The results indicated that the degradation of PG decreased the hydration capability of the dehydrated tissue. This study may provide useful information for further study on changes in the biomechanical property of articular cartilage in osteoarthritis.
[Show abstract][Hide abstract] ABSTRACT: In this paper, we propose a novel intensity-based similarity measure for medical image registration. Traditional intensity-based methods are sensitive to intensity distortions, contrast agent and noise. Although residual complexity can solve this problem in certain situations, relative modification of the parameter can generate dramatically different results. By introducing a specifically designed exponential weighting function to the residual term in residual complexity, the proposed similarity measure performed well due to automatically weighting the residual image between the reference image and the warped floating image. We utilized local variance of the reference image to model the exponential weighting function. The proposed technique was applied to brain magnetic resonance images, dynamic contrast enhanced magnetic resonance images (DCE-MRI) of breasts and contrast enhanced 3D CT liver images. The experimental results clearly indicated that the proposed approach has achieved more accurate and robust performance than mutual information, residual complexity and Jensen-Tsallis.
Computers in biology and medicine 10/2013; 43(10):1484-96. · 1.27 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: To propose a new method for automatic segmentation of manually determined knee articular cartilage into 9 subregions for T2 measurement.
The middle line and normal line were automatically obtained based on the outline of articular cartilage manually drawn by experienced radiologists. The region of articular cartilage was then equidistantly divided into 3 layers along the direct0ion of the normal line, and each layer was further equidistantly divided into 3 segments along the direction of the middle line. Finally the mean T2 value of each subregion was calculated. Bland-Altman analysis was used to evaluate the agreement between the proposed and manual subregion segmentation methods.
The 95% limits of agreement of manual and automatic methods ranged from -3.04 to 3.20 ms, demonstrating a narrow 95% limits of agreement (less than half of the minimum average). The coefficient of variation between the manual and proposed subregion methods was 4.04%.
The proposed subregion segmentation method shows a good agreement with the manual segmentation method and minimizes potential subjectivity of the manual method.
Nan fang yi ke da xue xue bao = Journal of Southern Medical University 06/2013; 33(6):874-877.
[Show abstract][Hide abstract] ABSTRACT: Early diagnosis of osteoarthritis (OA) is essential for preventing further cartilage destruction and decreasing severe complications. The aims of this study are to explore the relationship between OA pathological grades and quantitative acoustic parameters and to provide more objective criteria for ultrasonic microscopic evaluation of the OA cartilage.
Articular cartilage samples were prepared from rabbit knees and scanned using ultrasound biomicroscopy (UBM). Three quantitative parameters, including the roughness index of the cartilage surface (URI), the reflection coefficients from the cartilage surface (R) and from the cartilage-bone interface (Rbone) were extracted. The osteoarthritis grades of these cartilage samples were qualitatively assessed by histology according to the grading standards of International Osteoarthritis Institute (OARSI). The relationship between these quantitative parameters and the osteoarthritis grades was explored.
The results showed that URI increased with the OA grade. URI of the normal cartilage samples was significantly lower than the one of the OA cartilage samples. There was no significant difference in URI between the grade 1 cartilage samples and the grade 2 cartilage samples. The reflection coefficient of the cartilage surface reduced significantly with the development of OA (p < 0.05), while the reflection coefficient of the cartilage-bone interface increased with the increase of grade.
High frequency ultrasound measurements can reflect the changes in the surface roughness index and the ultrasound reflection coefficients of the cartilage samples with different OA grades. This study may provide useful information for the quantitative ultrasonic diagnosis of early OA.
[Show abstract][Hide abstract] ABSTRACT: OBJECTIVE: Concerns have been raised over x-ray radiation dose associated with repeated computed tomography (CT) scans for tumor surveillance and radiotherapy planning. In this paper, we present a low-dose CT image reconstruction method for improving low-dose CT image quality. The method proposed exploited rich redundancy information from previous normal-dose scan image for optimizing the non-local weights construction in the original non-local means (NLM)-based low-dose image reconstruction. The objective 3D low-dose volume and the previous 3D normal-dose volume were first registered to reduce the anatomic structural dissimilarity between the two datasets, and the optimized non-local weights were constructed based on the registered normal-dose volume. To increase the efficiency of this method, GPU was utilized to accelerate the implementation. The experimental results showed that this method obviously improved the image quality, as compared with the original NLM method, by suppressing the noise-induced artifacts and preserving the edge information.
Nan fang yi ke da xue xue bao = Journal of Southern Medical University 12/2011; 31(12):1974-80.
[Show abstract][Hide abstract] ABSTRACT: OBJECTIVE: To increase the resolution and signal-to-noise ratio (SNR) of magnetic resonance (MR) images, an adaptively regularized super-resolution reconstruction algorithm was proposed and applied to acquire high resolution MR images from 4 subpixel-shifted low resolution images on the same anatomical slice. The new regularization parameter, which allowed the cost function of the new algorithm to be locally convex within the definition region, was introduced by the piori information to enhance detail restoration of the image with a high frequency. The experiment results proved that the proposed algorithm was superior to other counterparts in achieving the reconstruction of low-resolution MR images.
Nan fang yi ke da xue xue bao = Journal of Southern Medical University 10/2011; 31(10):1705-8.
[Show abstract][Hide abstract] ABSTRACT: For accurate segmentation of the magnetic resonance (MR) images of meningioma, we propose a novel interactive segmentation method based on graph cuts. The high dimensional image features was extracted, and for each pixel, the probabilities of its origin, either the tumor or the background regions, were estimated by exploiting the weighted K-nearest neighborhood classifier. Based on these probabilities, a new energy function was proposed. Finally, a graph cut optimal framework was used for the solution of the energy function. The proposed method was evaluated by application in the segmentation of MR images of meningioma, and the results showed that the method significantly improved the segmentation accuracy compared with the gray level information-based graph cut method.
Nan fang yi ke da xue xue bao = Journal of Southern Medical University 07/2011; 31(7):1164-8.
[Show abstract][Hide abstract] ABSTRACT: OBJECTIVE: This paper presents a method for global feature extraction and the application of the boostmetric distance metric method for medical image retrieval. The global feature extraction method used the low frequency subband coefficient of the wavelet decomposition based on the non-tensor product coefficient for piecewise Gaussian fitting. The local features were extracted after semi-automatic segmentation of the lesion areas in the images in the database. The experimental verification of the method using 1688 CT images of the liver containing lesions of liver cancer, liver angioma, and liver cyst confirmed that this feature extraction method improved the detection rate of the lesions with good image retrieval performance.
Nan fang yi ke da xue xue bao = Journal of Southern Medical University 02/2011; 31(2):221-5.
[Show abstract][Hide abstract] ABSTRACT: Based on the fact that nonlocal means (NL-means) filtered image can likely produce an acceptable priori solution, we propose a sparse angular CT projection onto convex set (POCS) reconstruction using NL-means iterative modification. The new reconstruction scheme consists of two components, POCS and NL-means filter. In each phase of the sparse angular CT iterative reconstruction, we first used POCS algorithm to meet the identity and non-negativity of projection data, and then performed NL-means filter to the image obtained by POCS method for image quality improvement. Simulation experiments showed that the proposed POCS scheme can significantly improve the quality of sparse angular CT image by suppressing the noise and removing the streak-artifacts.
Nan fang yi ke da xue xue bao = Journal of Southern Medical University 10/2010; 30(10):2224-8.
[Show abstract][Hide abstract] ABSTRACT: With the utilization of diffusion tensor information of image voxels, a novel MRF (Markov Random Field) segmentation algorithm was proposed for diffusion tensor MRI (DT-MRI) images benefitted from the introduction of Frobenius norm. The comparison of the segmentation effects between the proposed algorithm and K-means segmentation algorithm for DT-MRI image was made, which showed that the new algorithm could segment the DT-MRI images more accurately than the K-means algorithm. Moreover, with the same segmentation algorithm of MRF, better outcomes were achieved in DT-MRI than in conventional MRI (T2WI) image.
Nan fang yi ke da xue xue bao = Journal of Southern Medical University 07/2010; 30(7):1562-4, 1572.
[Show abstract][Hide abstract] ABSTRACT: The medical CT scanner is rapidly evolving from the fan-beam mode to the cone-beam geometry mode. In this paper, a new cone-beam pseudo Lambda tomography was proposed based on the Noo's fan beam super-short scan formula and FDK framework. The proposed pseudo-LT algorithm, which avoids the computation of any PI line and any differential operation, has a significant practical implementation, thus leading to the images with quality improvement and reduced artifacts. The results in the simulation studies confirm the observation that the new algorithm can improve the image resolution over the traditional algorithms with noise projection data.
Nan fang yi ke da xue xue bao = Journal of Southern Medical University 10/2009; 29(10):2094-7.
[Show abstract][Hide abstract] ABSTRACT: OBJECTIVE: A new algorithm of adaptive super-resolution (SR) reconstruction based on the regularization parameter is proposed to reconstruct a high-resolution (HR) image from the low-resolution (LR) image sequence, which takes into full account the inaccurate estimates of motion error, point spread function (PSF) and the additive Gaussian noise in the LR image sequence. We established a novel nonlinear adaptive regularization function and analyzed experimentally its convexity to obtain the adaptive step size. This novel algorithm can effectively improve the spatial resolution of the image and the rate of convergence, which is verified by the experiment on optical images.
Nan fang yi ke da xue xue bao = Journal of Southern Medical University 05/2009; 29(4):656-8.
[Show abstract][Hide abstract] ABSTRACT: To improve the accuracy and efficiency of pulmonary nodule segmentation of thoracic CT image for computer-aided diagnostic (CAD) system, especially for those nodules adhering to the pleural or blood vessels.
We proposed the automatic process of pulmonary nodule segmentation, and using region growing method based on the contrast and gradient, the pulmonary nodule images were acquired. A self-adapted morphologic segmentation algorithm was presented for the unsuccessful nodule segmentation using region growing.
Experiments on clinical 2D pulmonary images showed that the solitary pulmonary nodules and those adhering to the pleural or blood vessels could all be segmented. This pulmonary nodule segmentation algorithm is feasible for automated segmentation of the thoracic CT images.
Nan fang yi ke da xue xue bao = Journal of Southern Medical University 01/2009; 28(12):2109-12.
[Show abstract][Hide abstract] ABSTRACT: This paper presents a new 3-D image registration method based on the principal component analysis (PCA). Compared with intensity-based registration methods using the whole volume intensity information, our approach utilizes PCA to estimate the centroid and principal axis, and completes the registration by aligning the centroid and principal axis. We evaluated the effectiveness of this approach by applying it to simulated and actual brain image data (MR, CT, PET, and SPECT). The experimental results indicate that the algorithm is effective, especially for registration of 3-D medical images.
Nan fang yi ke da xue xue bao = Journal of Southern Medical University 10/2008; 28(9):1591-3.
[Show abstract][Hide abstract] ABSTRACT: We present an alternative approach for precise reconstruction of the images from helical cone-beam projections combining Hilbert filter and Ramp filter.
Based on the Katsevich algorithm framework, the proposed algorithm combined the FDK-type algorithms and Katsevich algorithm for their respective advantages, to completely avoid the direct derivatives with respect to the coordinates on the detector plane.
The experimental results validated the accuracy of the new algorithm, and this approach significantly improved the resolution of the reconstructed images with much reduced artifacts.
The proposed reconstruction formula based on hybrid Hilbert-Ramp filter is an important development of Katsevich reconstruction formula, and the different forms of the Ramp filters can be designed to realize frequency modulation according to the actual clinical application.
Nan fang yi ke da xue xue bao = Journal of Southern Medical University 07/2008; 28(6):911-4.
[Show abstract][Hide abstract] ABSTRACT: OBJECTIVE: This paper describes a new method for extracting and segmenting intracranial structure from serial images of cerebral computerized tomography automatically. A region growing- and morphology-based approach was first developed to extract intracranial structures from the serial images of cerebral computerized tomography, and focusing on the problems of parameter initialization of the expectation maximization (EM) algorithm, an improved EM algorithm based on parameter- limited GMM was presented to segment the intracranial structures successfully. Experimental results of the algorithm showed that this method was effective for all cerebral computerized tomography images from bottom to top of the cerebrum.
Nan fang yi ke da xue xue bao = Journal of Southern Medical University 01/2008; 27(12):1805-8.
[Show abstract][Hide abstract] ABSTRACT: To improve the conventional reconstruction algorithm for PROPELLER MRI data, we propose a new algorithm based on fuzzy enhancement. The motion parameters were extracted from fuzzy enhanced images reconstructed through zero-padding strips. After motion compensation, the image was obtained through gridding reconstruction. The experiment results showed that this algorithm could estimate and compensate the motion more robustly and precisely, and the motion artifacts could be better suppressed to obtain improved image quality.
Nan fang yi ke da xue xue bao = Journal of Southern Medical University 06/2007; 27(5):618-20.
[Show abstract][Hide abstract] ABSTRACT: In the algorithms for image segmentation, the number of clusters (NOC), which impacts on the segmentation results, should be first solved, and its correct estimation both theoretically and in application is of much importance. The authors propose an adaptive total energy criterion (ATEC) based on Markov random fields (MRF). The correct NOC of different images can be obtained by minimizing the ATEC and the parameters in the criterion are estimated by expectation maximization algorithm and maximum pseudo-likelihood method. The experiments show that the NOC can be automatically detected by adjusting the parameters, and the segmentation with the estimated NOC can be obtained by the maximum a posteriori at the same time.
Nan fang yi ke da xue xue bao = Journal of Southern Medical University 08/2006; 26(7):959-62.
[Show abstract][Hide abstract] ABSTRACT: A fuzzy Markov random field (FMRF) model is established and a new algorithm based on FMRF for image segmentation proposed in this paper. This algorithm simultaneously deals with the fuzziness and randomness for effective acquisition of the prior knowledge of the images. A conventional Markov random field (CMRF) serves as a bridge between the FMRF, obviously a generalization of the CMRF, and the original images. The FMRF degenerates into the CMRF when no fuzziness is considered. The segmentation results are obtained by fuzzifying the image, updating the membership of prior FMRF based on the maximum posteriori criteria, and defuzzifying the image according to the maximum membership principle. The proposed algorithm can effectively filter the noise and eliminate partial volume effect when processing the degraded image to ensure more accurate image segmentation.
Nan fang yi ke da xue xue bao = Journal of Southern Medical University 06/2006; 26(5):579-83.
[Show abstract][Hide abstract] ABSTRACT: To propose a new method for content-based retrieval from medical CT image database on the basis of automatically extracted features of the images.
An automatic feature extraction method is proposed based on expectation-maximization algorithm. A CT image is represented by a set of regions, each of which is characterized by a fuzzy regional feature vector reflecting the grey level, texture, shape, and the cumulative distribution histogram feature of the region of interest (ROI) to efficiently describe the difference between the ROIs.
Compared with the submitted query image, the target images were retrieved in the order of similarity calculated by the proposed similarity measures.
The proposed technique for CT image retrieval is suitable for clinical application, with greater precision and efficiency for retrieval than the conventional methods.
Di 1 jun yi da xue xue bao = Academic journal of the first medical college of PLA 06/2004; 24(5):579-81.