Heterogeneity of echo-texture and lack of sharply delineated tissue boundaries in diagnostic ultrasound images make three-dimensional (3D) registration challenging, especially when the volumes to be registered are considerably different due to local changes. We implemented a novel computational method that optimally registers volumetric ultrasound image data containing significant and local anatomical differences. It is A Multi-stage, Multi-resolution, and Multi-volumes-of-interest Volume Registration Method. A single region registration is optimized first for a close initial alignment to avoid convergence to a locally optimal solution. Multiple sub-volumes of interest can then be selected as target alignment regions to achieve confident consistency across the volume. Finally, a multi-resolution rigid registration is performed on these sub-volumes associated with different weights in the cost function. We applied the method on 3D endovaginal ultrasound image data acquired from patients during biopsy procedure of the pelvic floor muscle. Systematic assessment of our proposed method through cross validation demonstrated its accuracy and robustness. The algorithm can also be applied on medical imaging data of other modalities for which the traditional rigid registration methods would fail.

Zebrafish (Danio rerio) is a powerful animal model used in many areas of genetics and disease research. Despite its advantages for cardiac research, the heartbeat pattern of zebrafish larvae under different stress conditions is not well documented quantitatively. Several effective automated heartbeat detection methods have been developed to reduce the workload for larva heartbeat analysis. However, most require complex experimental setups and necessitate direct observation of the larva heart. In this paper, we propose the Zebrafish Heart Rate Automatic Method (Z-HRAM), which detects and tracks the heartbeats of immobilized, ventrally positioned zebrafish larvae without direct larva heart observation. Z-HRAM tracks localized larva body deformation that is highly correlated with heart movement. Multiresolution dense optical flow-based motion tracking and principal component analysis are used to identify heartbeats. Here, we present results of Z-HRAM on estimating heart rate from video recordings of seizure-induced larvae, which were of low resolution (1024 × 760) and low frame rate (3 to 4 fps). Heartbeats detected from Z-HRAM were shown to correlate reliably with those determined through corresponding electrocardiogram and manual video inspection. We conclude that Z-HRAM is a robust, computationally efficient, and easily applicable tool for studying larva cardiac function in general laboratory conditions.

It is difficult for existing methods to measure the angular, bowing, and rotational knee alignments altogether in 2D radiograph images. We propose an automatic measurement method to quantify these knee alignments in three-dimensional (3D) bone models. Eight important anatomical landmarks of lower limb bones are used to establish lower extremity coordinate systems, in which we introduce seven parameters and discuss their measurements. These seven parameters are used to evaluate these knee alignments. Comparing with 2D radiograph methods, this method can measure both the distances and orientations of the bowing knee alignment, and can clearly visualize the rotational knee alignment.

Extraocular muscles (EOMs) enlargement affects the biomechanics of eye movement and is a key factor of several orbital diseases [1]. Identification of EOM enlargement is important for clinical diagnosis and treatment, however, accurate and efficient quantification of EOM anatomy is challenge. We present a fully automatic method to segment and reconstruct 3D model of the EOMs. We design a novel algorithm which uses superpixels (i.e. clusters of pixels) as the basic units for segmentation. After obtaining the segmented EOM boundaries, we reconstruct 3D models of the EOMs. Our proposed method on automatically reconstructing patient-specific EOM models can be applied in clinical diagnosis and surgical planning.

Spine surgeries are high risk operations which require the surgeons to have ample experiences. For young surgeons, effective and extensive training is critical. This paper presents a real time haptic spine surgical simulator that will be used to train residents, fellows and spine surgeons in a hospital training program. It provides a realistic environment for the trainees to practice spine surgeries and has the advantages of being interactive, low-cost, representative, and repeatable over conventional training approaches. Haptic Phantom offers the users force feedback, differentiating our system from other screen-based training systems. Computational efficiency was achieved by developing advanced graphical rendering methods. The volumetric data was classified into surface voxel cloud and inner voxel cloud by the adjacency graph which stored the relationship among voxels. To speed up the collision detection and real time rendering between the virtual surgical tools and the lumbar model, Octree-based algorithms and GPU technique were applied. To enhance the physical realism, three dimensional lumbar vertebrae models were reconstructed from CT images and associated with non-homogeneous bone density such that the rendered model best represents the spine anatomy and mechanics. We demonstrate system performance by conducting pedicle screw insertion.

This paper proposes a novel automatic method to segment extraocular muscles and orbital structures. Instead of conventional segmentation at the pixel level, superpixels at the structure level were used as the basic image processing unit. A region adjacency graph was built based on the neighborhood relationship among superpixels. Using Normalized Cuts on the region adjacency graph, we refined the segmentation by using a variety of features derived from the classical shape cues, including contours and continuity. To demonstrate the efficiency of the method, segmentation of Magnetic Resonance images of five healthy subjects was performed and analyzed. Three region-based image segmentation evaluation metrics were applied to quantify the automatic segmentation accuracy against manual segmentation. Our novel method could produce accurate and reproducible eye muscle segmentation.

Determining shape of the eyeball is important to diagnose eyeball disease like myopia. In this paper, we present an automatic approach to precisely reconstruct three dimensional geometric shape of eyeball from MR Images. The model development pipeline involved image segmentation, registration, B-Spline surface fitting and subdivision surface fitting, neither of which required manual interaction. From the high resolution resultant models, geometric characteristics of the eyeball can be accurately quantified and analyzed. In addition to the eight metrics commonly used by existing studies, we proposed two novel metrics, Gaussian Curvature Analysis and Sphere Distance Deviation, to quantify the cornea shape and the whole eyeball surface respectively. The experiment results showed that the reconstructed eyeball models accurately represent the complex morphology of the eye. The ten metrics parameterize the eyeball among different subjects, which can potentially be used for eye disease diagnosis.

Cloud computing is attracting the attention of the scientific community. In this paper, we develop a new cloud-based computing system in the Windows Azure platform that allows users to use the Zeolite Structure Predictor (ZSP) model through a Web browser. The ZSP is a novel machine learning approach for classifying zeolite crystals according to their framework type. The ZSP can categorize entries from the Inorganic Crystal Structure Database into 41 framework types. The novel automated system permits a user to calculate the vector of descriptors used by ZSP and to apply the model using the Random Forestalgorithm for classifying the input zeolite entries. The workflow presented here integrates executables in Fortran and Python for number crunching with packages such as Weka for data analytics and Jmol forWeb-based atomistic visualization in an interactive compute system accessed through the Web. The compute system is robust and easy to use. Communities of scientists, engineers, and students knowledgeable in Windows-based computing should find this new workflow attractive and easy to be implemented in scientific scenarios in which the developer needs to combine heterogeneous components. Copyright (c) 2012 John Wiley & Sons, Ltd.

In order to address the problem of detecting and locating the anterior cruciate ligament of human's knee in medical image and promote the study of its reconstruction operation, this paper proposes a hierarchical detection based method to locate the anterior cruciate ligament. The location task is performed in the global and the local detections successively. The features are selected according to the type of image samples, and the corresponding global and local detectors are built based on the random forests respectively to first find the entire region of the anterior cruciate ligament and then recognize its definite area. Experimental results based on the real MRI images validate the effectiveness and accuracy of our method.

To allow an efficient assessment of lower limb alignment and hip fracture, 3D lower limb bone models are reconstructed from patient-specific computed tomography images. The 3D feature-construction system shown here automatically extracts 3D features (centers, auxiliary points, axes, and planes). No other work exists that automatically marks all the features for the lower extremities.

Lower extremity deformity can cause joint pain and malfunction. Patients with severe deformity usually need a correction surgery or total knee replacement (TKR) surgery to realign the orientation of the femur and tibia. Lower extremity deformity needs to be accurately assessed before any clinical decision can be made. In practice, physicians and radiologists rely mainly on X-ray images to evaluate the deformity, and CT is used in complex cases only. Manual assessment on X-rays is tedious, time consuming and inaccurate [1]. Computer aided diagnosis was proved efficient to understand patients’ anatomy, analyze lower limb deformity and plan the possible surgery [2]. However, notable interactive works were required during the identification of the anatomic features, and the accuracy was unguaranteed when the physician is not familiar with the diagnosis software. In this study we developed an automatic assessment system to identify patients’ anatomic features and quantify lower extremity deformity.

We develop 3D automatic feature construction system to efficiently quantify different types of the lower limb alignment and the hip fracture. This system has the potential to provide useful information regarding the risk of knee and hip osteoarthritis, and should be considered an alternative method for the measurement of the lower limb alignment and the hip fracture. Our newly defined parameters (hip-knee-ankle, condylar-hip, plateau-ankle, condylar-plateau, condylar-tibia angles and mechanical axis deviation) completely exhibit the lower limb alignment and are coincident with the physicians' diagnosis. Neck-shaft angle measured for the hip fracture is also coincident with physicians' knowledge. Our novel quantitative and visual assessments of these parameters demonstrate that the automatic system can obviously improve the performance of the lower limb alignment assessment and the hip fracture diagnosis.

The detection of the lumen contour in the Intravascular Ultrasound (IVUS) image plays a very important part in assessing atherosclerosis. However, for the images at a high sampling frequency, the blood signals make it difficult to detect the lumen contours. In this paper, a new segmentation method is proposed and implemented that detects the lumen contour in IVUS images automatically. The method is based on the difference of the texture features between the blood signals and the vessel wall. During preprocessing of the raw IVUS images, the method successfully removed the artificial noise, which was caused by the sampling catheter. After that, the lumen texture features and the vessel wall texture features were able to be distinguished through applying the Gabor wavelet transformation. Based on the distinguished texture features, the lumen contour was detected and refined smoothly. The experiment results indicate that the lumen contour in the raw IVUS image can be detected completely automatically and accurately.

Accurate measurement of lower limb alignment is vital for orthopedic surgery planning. Based on the 3D lower limb bone models reconstructed from CT images, a 3D feature construction system is developed to find the lower limb alignment. This paper presents new methods to automatically identify the anatomic axes, mechanical axes, joint center points, and reference planes of the lower extremities. As far as we know, there is no other system that can construct all these features automatically. With the above identified features, our system can calculate the related angles of the lower limb alignment accurately for orthopedic knee surgery planning. We applied our system on two groups of specimen (with 6 pairs of lower limbs), and the resulting related angles are identical to the actual angles of the lower limb alignment.

Improper radiographic images and manual measurement lead to the incorrect evaluation of the torsional alignment of the knee joint. The paper presents the simple, reliable, and robust methods to assess this alignment in 3D visualization. All methods work on 3D bone models reconstructed from CT scan images and are driven by automatically identified bony landmarks and medial axes of the long bones with virtual reality technology. The measured torsional angles of the femur and tibia have important clinical functions to diagnose the torsional alignment. Experiment results show the proposed approach is efficient, robust and reproducible.

In this paper,a novel rendering algorithm for implicit surfaces which are reconstructed from point cloud data by using a particle system was presented.First,an alternative initial technique based on bundles of parallel lines was used to find initial points that were evenly distributed on the surface.Because of its characteristics,the usual split-and-death criterion of particle system was not needed.Second,each elliptical particle was moved towards a progressively lower energy state using a conjugate gradient method,which replaced the gradient-descent as an optimization method to avoid very long convergence time and irreconcilable oscillation around the minimum.Third,a greedy selection strategy was used to choose a subset of active particles which guarantee a hole-free approximation.Finally,a relaxation process further improves the curvature driven anisotropic particle sampling.The proposed elliptical particles were specially designed for splat-based representation and could be directly converted into elliptical surface Splats without any modification,thus high-performance rendering of complex implicit surfaces can be obtained.

The energy sources hold a very large proportion in modern logistics. As a kind of clean energy source with high efficiency, the natural gas has getting more and more attention all over the world while the international oil price goes upward unceasingly and the environmental protection pressure becomes stronger and stronger. According to the energy strategy of China, 11 natural gas terminal stations will be constructed in Guangdong, Fujian, Shandong, Zhejiang, etc. to receive natural gas imported from long-distance. The large scale low temperature liquefied natural gas (LNG) tanks are specialized logistics Infrastructure; it is undoubted that their safety must be guaranteed. The liquefied natural gas tank is a complex heat transfer system. The temperature fields of large-scale low temperature liquefied natural gas tanks which independently designed by Chinese are analyzed in this paper. By using Finite Element Method, considering nonlinear heat conduction and convection, three kinds of boundary conditions and the symmetry of the tank, a model including prestressed concrete, insulating layer and LNG is built by using ANSYS software. Then, the temperature fields and temperature gradient distributions are computed at highest and lowest environmental temperatures under normal working condition. The results show that the heat preservation performance of design scheme is effective. The modeling and analysis method is helpful to other low temperature tanks.

Several severe accidents have occurred in metro transportation, the attention for safety has increased enormously. The paper introduces the method to combine metro traffic safety with Virtual Reality technology in urban rail traffic system, studies the metro fire characteristics and causes. Puts forward a method to set up the metro virtual environment for simulation by using Virtual Reality technology, which is helpful to deal with potential hazards in advance. On this foundation, by using 3DS MAX and OpenGL graphics programming techniques, the rendering pipeline of virtual metro station scene is designed. The virtual metro station scene is rendered and can be roamed and implemented in real time. The dynamical fire effect is rendered in the virtual metro station by using the real time fire simulation algorithm based on the particle system, and the locomotive and metro station fire are simulated. Preliminary fire simulation framework of the virtual metro station is established.

In this paper, we present a novel rendering algorithm for implicit surfaces by using a particle system. First, an alternative initial technique based on bundles of parallel lines is used to find initial points that are evenly distributed on the surface. Because of its characteristics, the usual split-and-death criterion of particle system is not needed. Second, we move each elliptical particle towards a progressively lower energy state using a conjugate gradient method. Third, a greedy selection strategy is used to choose a subset of active particles which guarantee a hole-free approximation. Finally, a relaxation process further improves the curvature driven anisotropic particle sampling. Our elliptical particles are especially designed for splat-based representation and can be directly converted into elliptical surface splats as rendering primitives without any modification, thus we are able to obtain high-performance and high-quality rendering of complex implicit surfaces.

We presented our work on constructing an interactive virtual inner ear surgery simulation system. The work was based on exploiting Cult3D's Designer functionalities to integrate multiple activities and objects in an inner ear. The system achieves inner ear virtual surgery and browsing with magnification.