11879758
Correction of bias field in MR images using singularity function analysis
A new approach for correcting bias field in magnetic resonance (MR) images is proposed using the mathematical model of singularity function analysis (SFA), which represents a discrete signal or its spectrum as a weighted sum of singularity functions. Through this model, an MR image's low spatial frequency components corrupted by a smoothly varying bias field are first removed, and then reconstructed from its higher spatial frequency components not polluted by bias field. The thus reconstructed image is then used to estimate bias field for final image correction. The approach does not rely on the assumption that anatomical information in MR images occurs at higher spatial frequencies than bias field. The performance of this approach is evaluated using both simulated and real clinical MR images.
27779170
Study of Dempster-Shafer theory for image segmentation applications
Abstract This paper addresses a new knowledge model using the Dempster–Shafer's theory of evidence for image segmentation and fusion. The salient aspects of the proposed method are at two levels. Firstly, mass distributions in Dempster–Shafer theory are directly derived from image histograms. Secondly, the fusion process does not start from one single frame of discernment, as it was done in most of the previously reported works, but does start from first defining two independent frames of discernment associated with the two images to be fused, and then combining them for forming a new frame of discernment. The strategy used to define mass distributions in the combined framework is discussed in detail. The proposed fusion method is illustrated in the context of image segmentation. The obtained results show the robustness of the method.
41212780
Real-time rendering of 3D medical data sets
Interactive exploration of three dimension (3D) medical data sets is required by many applications, but the huge amount of computational time and storage space needed for rendering do not allow the visualization of large medical data sets by now. In this paper we present a new algorithm for rendering large medical data sets at interactive frame rates on standard PC hardware. The input data is converted into a compressed hierarchical wavelet representation in a preprocessing step. During rendering, the wavelet representation is decompressed on-the-fly and rendered using hardware texture mapping. The level of detail used for rendering is adapted to the local frequency spectrum of the data and its position relative to the viewer. Using a prototype implementation of the algorithm we were able to perform an interactive walkthrough of large medical data sets on a single of-the-shelf PC.
2014328
A 3D reconstruction algorithm based on 3D deformable atlas
Reconstructing 3D models from 2D images is an important problem in visualization, particularly in medical and biological visualization. We present a new algorithm for 3D model reconstruction from 2D-images. In our approach, a 3D atlas is deformed to fit the feature data set in the target images. Compared with existing 3D reconstruction techniques, our approach is more efficient and easier to control. The resultant model has good inherent connectivity and smoothness. The resolution of the final model is determined by the resolution of reference model rather than the dataset. The model generates highly detailed view with a limited number of images. This algorithm has been successfully applied to medical simulation.
42202570
Improving ASM Search Using Mixture Models for Grey-Level Profiles
The use of Active Shape Models (ASM) has been shown to be an efficient approach to image interpretation and pattern recognition. In ASM, grey-level profiles at landmarks are modelled as a Gaussian distribution. Mahalanobis distance from a sample profile to the model mean is used to locate the best position of a given landmark during ASM search. We present an improved ASM methodology, in which the profiles are modelled as a mixture of Gaussians, and the probability that a sample is from the distribution is calculated using the probability density function (pdf) of the mixture model. Both improved and original ASM methods were tested on synthetic and real data. The performance comparison demonstrates that the improved ASM method is more generic and robust than the original approach.
14174120
Overlay Multicast with Inferred Link Capacity Correlations
We model the overlay using linear capacity constraints, which accurately and succinctly capture overlay link correlations. We show that finding a maximum-bandwidth multicast tree in an overlay with LCC is NP-complete and propose an efficient distributed heuristics algorithm.
15428033
On Optimal Physical Carrier Sensing: Theoretical Analysis and Protocol Design
Traditional Physical Carrier Sensing (PCS), which aims at eliminating hidden terminals in wireless networks, causes too many exposed terminals and deteriorates the throughput per user seriously. Some existing work has proven that an aggressive PCS can improve the throughput by balancing the tradeoff between hidden terminals and exposed terminals. However, little work has been conducted to optimize the PCS according to the network conditions. In this paper, we develop an analytical model to study the behaviors of a user in the aggressive PCS scenario, with which the optimal PCS threshold can be derived. Then, to avoid the complicated computation, we present a heuristic algorithm, in which the parameters required for PCS tuning are estimated by the carrier sensing mechanism employed in IEEE 802.11. Simulation results show that the proposed heuristic algorithm can make the PCS threshold approach to the theoretical optimal one. Moreover, the proposed heuristic algorithm can obtain significant throughput gain compared to the traditional PCS threshold setting solutions.
18332258
Simulation and visualization of knee joint contact using deformable model
We introduce a new method for simulating and visualizing femur-menisci contact in human knee joint using computer generated 3D models. Knee joint contact analysis is important for understanding or solving clinical problems. Traditional knee biomechanical analysis is mostly conducted on a cadaver or based on mathematical descriptions. The lack of 3D graphical visualization makes it difficult to understand the analysis and integrate it into virtual medical simulation systems. Our method addresses this problem by using patient specific knee models and motion data to obtain the contact area information. Our method employs a new physics-based algorithm for menisci deformation and can calculate and visualize knee-menisci contact areas during gait cycles.