Prediction based collaborative trackers (PCT): a robust and accurate approach toward 3D medical object tracking

Robust and fast 3D tracking of deformable objects, such as heart, is a challenging task because of the relatively low image contrast and speed requirement. Many existing 2D algorithms might not be directly applied on the 3D tracking problem. The 3D tracking performance is limited due to dramatically increased data size, landmarks ambiguity, signal drop-out or complex nonrigid deformation. In this paper, we present a robust, fast, and accurate 3D tracking algorithm: prediction based collaborative trackers (PCT). A novel one-step forward prediction is introduced to generate the motion prior using motion manifold learning. Collaborative trackers are introduced to achieve both temporal consistency and failure recovery. Compared with tracking by detection and 3D optical flow, PCT provides the best results. The new tracking algorithm is completely automatic and computationally efficient. It requires less than 1.5 s to process a 3D volume which contains millions of voxels. In order to demonstrate the generality of PCT, the tracker is fully tested on three large clinical datasets for three 3D heart tracking problems with two different imaging modalities: endocardium tracking of the left ventricle (67 sequences, 1134 3D volumetric echocardiography data), dense tracking in the myocardial regions between the epicardium and endocardium of the left ventricle (503 sequences, roughly 9000 3D volumetric echocardiography data), and whole heart four chambers tracking (20 sequences, 200 cardiac 3D volumetric CT data). Our datasets are much larger than most studies reported in the literature and we achieve very accurate tracking results compared with human experts' annotations and recent literature.     

Detection and measurement of fetal anatomies from ultrasound images using a constrained probabilistic boosting tree

We propose a novel method for the automatic detection and measurement of fetal anatomical structures in ultrasound images. This problem offers a myriad of challenges, including: difficulty of modeling the appearance variations of the visual object of interest, robustness to speckle noise and signal dropout, and large search space of the detection procedure. Previous solutions typically rely on the explicit encoding of prior knowledge and formulation of the problem as a perceptual grouping task solved through clustering or variational approaches. These methods are constrained by the validity of the underlying assumptions and usually are not enough to capture the complex appearances of fetal anatomies. We propose a novel system for fast automatic detection and measurement of fetal anatomies that directly exploits a large database of expert annotated fetal anatomical structures in ultrasound images. Our method learns automatically to distinguish between the appearance of the object of interest and background by training a constrained probabilistic boosting tree classifier. This system is able to produce the automatic segmentation of several fetal anatomies using the same basic detection algorithm. We show results on fully automatic measurement of biparietal diameter (BPD), head circumference (HC), abdominal circumference (AC), femur length (FL), humerus length (HL), and crown rump length (CRL). Notice that our approach is the first in the literature to deal with the HL and CRL measurements. Extensive experiments (with clinical validation) show that our system is, on average, close to the accuracy of experts in terms of segmentation and obstetric measurements. Finally, this system runs under half second on a standard dual-core PC computer.     

An information fusion framework for robust shape tracking

Existing methods for incorporating subspace model constraints in shape tracking use only partial information from the measurements and model distribution. We propose a unified framework for robust shape tracking, optimally fusing heteroscedastic uncertainties or noise from measurement, system dynamics, and a subspace model. The resulting nonorthogonal subspace projection and fusion are natural extensions of the traditional model constraint using orthogonal projection. We present two motion measurement algorithms and introduce alternative solutions for measurement uncertainty estimation. We build shape models offline from training data and exploit information from the ground truth initialization online through a strong model adaptation. Our framework is applied for tracking in echocardiograms where the motion estimation errors are heteroscedastic in nature, each heart has a distinct shape, and the relative motions of epicardial and endocardial borders reveal crucial diagnostic features. The proposed method significantly outperforms the existing shape-space-constrained tracking algorithm. Due to the complete treatment of heteroscedastic uncertainties, the strong model adaptation, and the coupled tracking of double-contours, robust performance is observed even on the most challenging cases.     

Sequential kernel density approximation and its application to real-time visual tracking

Visual features are commonly modeled with probability density functions in computer vision problems, but current methods such as a mixture of Gaussians and kernel density estimation suffer from either the lack of flexibility, by fixing or limiting the number of Gaussian components in the mixture, or large memory requirement, by maintaining a non-parametric representation of the density. These problems are aggravated in real-time computer vision applications since density functions are required to be updated as new data becomes available. We present a novel kernel density approximation technique based on the mean-shift mode finding algorithm, and describe an efficient method to sequentially propagate the density modes over time. While the proposed density representation is memory efficient, which is typical for mixture densities, it inherits the flexibility of non-parametric methods by allowing the number of components to be variable. The accuracy and compactness of the sequential kernel density approximation technique is illustrated by both simulations and experiments. Sequential kernel density approximation is applied to on-line target appearance modeling for visual tracking, and its performance is demonstrated on a variety of videos.     

Reliable Detection of Overtaking Vehicles Using Robust Information Fusion

Early detection of overtaking vehicles is an important task for vision-based driver assistance systems. Techniques utilizing image motion are likely to suffer from spurious image structures caused by shadows and illumination changes, let alone the aperture problem. To achieve reliable detection of overtaking vehicles, the authors have developed a robust detection method, which integrates dynamic scene modeling, hypothesis testing, and robust information fusion. A robust fusion algorithm, based on variable bandwidth density fusion and multiscale mean shift, is introduced to obtain reliable motion estimation against various image noise. To further reduce detection error, the authors model the dynamics of road scenes and exploit useful constraints induced by the temporal coherence in vehicle overtaking. The proposed solution is integrated into a monocular vision system onboard for obstacle detection. Test results have shown superior performance achieved by the new method     

Interference aware cooperative routing for wireless ad hoc networks

In this paper, we propose a cooperative approach for routing in wireless ad hoc networks. Our solution improves the interference distribution in the network, with an immediate positive impact on the throughput performance and energy efficiency. In determining new routes, we consider not only the cost associated with the current route, but also the potential interference impact of the route on the neighboring nodes.We use this cooperative approach to determine routes for CDMA ad hoc networks, which are known to be severely limited in performance by the near–far effect. Our simulation results using cooperative routing show an improvement in throughput of up to 60% compared to the classic minimum energy routing approach. This improvement is achieved at the expense of only a slight increase in the average energy per bit transmission for an end-to-end path.     

Cross‐layer MAC design for location‐aware wireless sensor networks

In this paper, we propose an optimization of MAC protocol design for wireless sensor networks, that accounts for cross‐layering information, in terms of location accuracy for nodes and residual energy levels. In our proposed solution we encode this cross‐layer information within a decreasing backoff function in the MAC. The protocol is optimized by appropriately selecting priority window lengths, and we have shown that accurate cross‐layer information plays a crucial role in achieving an optimal performance at the MAC layer level. The estimation accuracy can be characterized spatially using a location reliability probability distribution function. We show that this distribution function greatly influences the design of the optimal backoff window parameters, and the overall throughput performance of the MAC protocol. Copyright © 2010 John Wiley & Sons, Ltd.     

A probabilistic model for automatic segmentation of the esophagus in 3-D CT scans

Being able to segment the esophagus without user interaction from 3-D CT data is of high value to radiologists during oncological examinations of the mediastinum. The segmentation can serve as a guideline and prevent confusion with pathological tissue. However, limited contrast to surrounding structures and versatile shape and appearance make segmentation a challenging problem. This paper presents a multistep method. First, a detector that is trained to learn a discriminative model of the appearance is combined with an explicit model of the distribution of respiratory and esophageal air. In the next step, prior shape knowledge is incorporated using a Markov chain model. We follow a "detect and connect" approach to obtain the maximum a posteriori estimate of the approximate esophagus shape from hypothesis about the esophagus contour in axial image slices. Finally, the surface of this approximation is nonrigidly deformed to better fit the boundary of the organ. The method is compared to an alternative approach that uses a particle filter instead of a Markov chain to infer the approximate esophagus shape, to the performance of a human observer and also to state of the art methods, which are all semiautomatic. Cross-validation on 144 CT scans showed that the Markov chain based approach clearly outperforms the particle filter. It segments the esophagus with a mean error of 1.80 mm in less than 16 s on a standard PC. This is only 1 mm above the interobserver variability and can compete with the results of previously published semiautomatic methods.     

Adaptive Channel Allocation Spectrum Etiquette for Cognitive Radio Networks

In this work, we propose a game theoretic framework to analyze the behavior of cognitive radios for distributed adaptive channel allocation. We define two different objective functions for the spectrum sharing games, which capture the utility of selfish users and cooperative users, respectively. Based on the utility definition for cooperative users, we show that the channel allocation problem can be formulated as a potential game, and thus converges to a deterministic channel allocation Nash equilibrium point. Alternatively, a no-regret learning implementation is proposed for both scenarios and it is shown to have similar performance with the potential game when cooperation is enforced, but with a higher variability across users. The no-regret learning formulation is particularly useful to accommodate selfish users. Non-cooperative learning games have the advantage of a very low overhead for information exchange in the network. We show that cooperation based spectrum sharing etiquette improves the overall network performance at the expense of an increased overhead required for information exchange.