Weijian Cong

External force back-projective composition and globally deformable optimization for 3-D coronary artery reconstruction

The clinical value of the 3D reconstruction of a coronary artery is important for the diagnosis and intervention of cardiovascular diseases. This work proposes a method based on a deformable model for reconstructing coronary arteries from two monoplane angiographic images acquired from different angles. First, an external force back-projective composition model is developed to determine the external force, for which the force distributions in different views are back-projected to the 3D space and composited in the same coordinate system based on the perspective projection principle of x-ray imaging. The elasticity and bending forces are composited as an internal force to maintain the smoothness of the deformable curve. Second, the deformable curve evolves rapidly toward the true vascular centerlines in 3D space and angiographic images under the combination of internal and external forces. Third, densely matched correspondence among vessel centerlines is constructed using a curve alignment method. The bundle adjustment method is then utilized for the global optimization of the projection parameters and the 3D structures. The proposed method is validated on phantom data and routine angiographic images with consideration for space and re-projection image errors. Experimental results demonstrate the effectiveness and robustness of the proposed method for the reconstruction of coronary arteries from two monoplane angiographic images. The proposed method can achieve a mean space error of 0.564 mm and a mean re-projection error of 0.349 mm.

Adaptive Tensor-Based Principal Component Analysis for Low-Dose CT Image Denoising.

Computed tomography (CT) has a revolutionized diagnostic radiology but involves large radiation doses that directly impact image quality. In this paper, we propose adaptive tensor-based principal component analysis (AT-PCA) algorithm for low-dose CT image denoising. Pixels in the image are presented by their nearby neighbors, and are modeled as a patch. Adaptive searching windows are calculated to find similar patches as training groups for further processing. Tensor-based PCA is used to obtain transformation matrices, and coefficients are sequentially shrunk by the linear minimum mean square error. Reconstructed patches are obtained, and a denoised image is finally achieved by aggregating all of these patches. The experimental results of the standard test image show that the best results are obtained with two denoising rounds according to six quantitative measures. For the experiment on the clinical images, the proposed AT-PCA method can suppress the noise, enhance the edge, and improve the image quality more effectively than NLM and KSVD denoising methods.

Energy back-projective composition for 3-D coronary artery reconstruction

This paper presents a novel energy back-projective composition model (EBPCM) for 3-D reconstruction of the coronary arteries from two mono-plane angiographic images. A major problem with the commonly used parameter deformable model is that the predefined correspondences may become non-strict matching after the curve evolution, which generally leads to large extra calculation errors. In this study, the energy field in the image is back-projected to 3-D space and decomposed into three independent components in the world coordinates centered at the iso-center of the C-arm. Then, the components from different views are composited together according to the rotation and scaling relationship of the imaging angles. The composited energy field hence is utilized as the external force to control the evolution of the vascular structure in 3-D space. As the driving force is iteratively updated according to energy in the two projection images, the non-strict matching can be effectively avoided. Also, the proposed method is very flexible, which can be composited with any energy fields such as Generalized Gradient Vector Flow (GGVF) and Potential Energy (PE) etc. Experiments demonstrate that the proposed method is very effective and robust, when using GGVF as the external force, the reconstruction RMS error can be reduced to about 0.595mm in the 3-D space.

Automatic liver segmentation based on appearance and context information

BackgroundAutomated image segmentation has benefits for reducing clinicians’ workload, quicker diagnosis, and a standardization of the diagnosis.MethodsThis study proposes an automatic liver segmentation approach based on appearance and context information. The relationship between neighboring pixels in blocks is utilized to estimate appearance information, which is used for training the first classifier and obtaining the probability distribution map. The map is used for extracting context information, along with appearance features, to train the next classifier. The prior probability distribution map is achieved after iterations and refined through an improved random walk for liver segmentation without user interaction.ResultsThe proposed approach is evaluated using CT images with eight contemporary approaches, and it achieves the highest VOE, RVD, ASD, RMSD and MSD. It also achieves a high average score of 76 using the MICCAI-2007 Grand Challenge scoring system.ConclusionsExperimental results show that the proposed method is superior to eight other state of the art methods.

Geometrical force constraint method for vessel and x-ray angiogram simulation

This study proposes a novel geometrical force constraint method for 3-D vasculature modeling and angiographic image simulation. For this method, space filling force, gravitational force, and topological preserving force are proposed and combined for the optimization of the topology of the vascular structure. The surface covering force and surface adhesion force are constructed to drive the growth of the vasculature on any surface. According to the combination effects of the topological and surface adhering forces, a realistic vasculature can be effectively simulated on any surface. The image projection of the generated 3-D vascular structures is simulated according to the perspective projection and energy attenuation principles of X-rays. Finally, the simulated projection vasculature is fused with a predefined angiographic mask image to generate a realistic angiogram. The proposed method is evaluated on a CT image and three generally utilized surfaces. The results fully demonstrate the effectiveness and robustness of the proposed method.

Multiresolution generalized N dimension PCA for ultrasound image denoising

BackgroundUltrasound images are usually affected by speckle noise, which is a type of random multiplicative noise. Thus, reducing speckle and improving image visual quality are vital to obtaining better diagnosis.MethodIn this paper, a novel noise reduction method for medical ultrasound images, called multiresolution generalized N dimension PCA (MR-GND-PCA), is presented. In this method, the Gaussian pyramid and multiscale image stacks on each level are built first. GND-PCA as a multilinear subspace learning method is used for denoising. Each level is combined to achieve the final denoised image based on Laplacian pyramids.ResultsThe proposed method is tested with synthetically speckled and real ultrasound images, and quality evaluation metrics, including MSE, SNR and PSNR, are used to evaluate its performance.ConclusionExperimental results show that the proposed method achieved the lowest noise interference and improved image quality by reducing noise and preserving the structure. Our method is also robust for the image with a much higher level of speckle noise. For clinical images, the results show that MR-GND-PCA can reduce speckle and preserve resolvable details.

Fast and Automatic Ultrasound Simulation from CT Images

Ultrasound is currently widely used in clinical diagnosis because of its fast and safe imaging principles. As the anatomical structures present in an ultrasound image are not as clear as CT or MRI. Physicians usually need advance clinical knowledge and experience to distinguish diseased tissues. Fast simulation of ultrasound provides a cost-effective way for the training and correlation of ultrasound and the anatomic structures. In this paper, a novel method is proposed for fast simulation of ultrasound from a CT image. A multiscale method is developed to enhance tubular structures so as to simulate the blood flow. The acoustic response of common tissues is generated by weighted integration of adjacent regions on the ultrasound propagation path in the CT image, from which parameters, including attenuation, reflection, scattering, and noise, are estimated simultaneously. The thin-plate spline interpolation method is employed to transform the simulation image between polar and rectangular coordinate systems. The Kaiser window function is utilized to produce integration and radial blurring effects of multiple transducer elements. Experimental results show that the developed method is very fast and effective, allowing realistic ultrasound to be fast generated. Given that the developed method is fully automatic, it can be utilized for ultrasound guided navigation in clinical practice and for training purpose.

Real-Time Non-invasive Imaging of Subcutaneous Blood Vessels

Automatic, fast and accurate extraction of the blood vessel is an important task in the image-aided diagnosis of disease. In this paper, we describe a novel superficial vessel imaging and projecting system. First, the superficial vessel of human arm is captured by NIR imaging technique, and then three pre-processing algorithms, including hair removal, non-uniform illumination correction and vessel enhancement, are developed to strengthen the vessel image. Second, a model-based binarization method is proposed to detect vessel-like structures. And then the mathematic morphological and connected component refinement methods are integrated to remove small vessel noises. Experimental results demonstrate that our system can imaging superficial vessels on real-time. The proposed system does not need any human interaction, which hence can be used in clinical venipuncture practices.