14869645
Multi-modality model-based registration in the cardiac domain
Recent advances in CT technology have made possible the acquisition of high resolution images of the heart in which important anatomical features such as the coronary vessels are visible. The state of the art in MR Imaging techniques, on the other hand, typically provides lower resolution but can facilitate the differentiation of viable (healthy) cardiac tissue from infarcted (injured) tissue. The registration of CT and MR images of the same heart permit the association of anatomy with function. As an example, given an infarcted region, the coronary vessels responsible for irrigating that region may be isolated and targeted for treatment (e.g., coronary bypass). We demonstrate a software package, PROTEUS, which is capable of registering CT and MR images. Since the manifestation of cardiac tissue in the two modalities differs so greatly we rely on features to perform the matching. Specifically, we employ a model-based approach that relies on segmented contours delineating the inner and outer borders of the left ventricle (LV). Given these contours, our technique is completely automatic.
30968721
Tracking Deformable Templates Using a Shortest Path Algorithm
This paper proposes a new technique to track deformable templates. We extend the typical graph algorithms that have been used for active contour recovery to incorporate shape information. The advantage of graph algorithms is that they are guaranteed to find the global minimum of the energy function. The difficulty with their traditional use for active contours is that they consider only two pixels at a time when recovering the contour, making it impossible to enforce shape constraints. We define the deformable template as a polygonal contour, demonstrate the proper mapping between the image, the contour, and a graph, and show how to apply Dijkstra's algorithm to track contours in image sequences. Examples are shown for deforming contours, articulated objects, and smooth contours being tracked in simple and complicated backgrounds. We also provide an analysis of the computational requirements.
11252982
Segmentation of range images as the search for geometric parametric models
Segmentation of range images has long been considered in computer vision as an important but extremely difficult problem. In this paper we present a new paradigm for the segmentation of range images into piecewise continuous surfaces. Data aggregation is performed via model recovery in terms of variable-order bi-variate polynomials using iterative regression. Model recovery is initiated independently in regularly placed seed regions in the image. All the recovered models are potential candidates for the final description of the data. Selection of the models is defined as a quadratic Boolean problem, and the solution is sought by the WTA (winner-takes-all) technique, which turns out to be a good compromise between the speed of computation and the accuracy of the solution. The overall efficiency of the method is achieved by combining model recovery and model selection in an iterative way. Partial recovery of the models is followed by the selection (optimization) procedure and only the “best” models are allowed to develop further.The major novelty of the approach lies in an effective combination of simple component algorithms, which stands in contrast to methods which attempt to solve the problem in a single processing step using sophisticated means. We present the results on several real range images.
9910171
Semantics and CBIR: a medical imaging perspective
Medical CBIR (content-based image retrieval) applications pose unique challenges but at the same time offer many new opportunities. On one hand, while one can easily understand news or sports videos, a medical image is often completely incomprehensible to untrained eyes. On the other hand, semantics in the medical domain is much better defined and there is a vast accumulation of formal knowledge representations that could be exploited to support semantic search for any specialty areas in medicine.
 In this paper, however, we will not dwell on any one particular specialty area, but rather address the question of how to support scalable semantic search across the whole of medical CBIR field: what are the advantages to take and gaps to fill, what are the key enabling technologies, and the critical success factor from an industrial point of view.
 In terms of enabling technologies, we discuss three aspects: 1. anatomical, disease, and contextual semantics, and their representations using ontologies; 2. scalable image analysis and tagging algorithms; and 3. ontological reasoning and its role in guiding and improving image analysis and retrieval.
 More specifically, for ontological representation of medical imaging semantics, we discuss the potential use of FMA, RadLex, ICD, and AIM. For scalable image analysis we present a learning-based anatomy detection and segmentation framework using distribution-free priors. It is easily adaptable to different anatomies and different imaging modalities.
5230577
A cooperative framework for segmentation using 2D active contours and 3D hybrid models as applied to branching cylindrical structures
Hybrid models are powerful tools for recovery in that they simultaneously provide a gross parametric as well as a detailed description of an object. However, it is difficult to directly employ hybrid models in the segmentation process since they are not guaranteed to locate the optimal boundaries in cross-sectional slices. Propagating 2D active contours from slice to slice, on the other hand, to delineate an object's boundaries is often effective, but may run into problems when the object's topology changes, such as at bifurcations or even in areas of high curvature. Here, we present a cooperative framework to exploit the positive aspects of both 3D hybrid model and 2D active contour approaches for segmentation and recovery. In this framework the user-defined parametric component of a 3D hybrid model provides constraints for a set of 2D segmentations performed by active contours. The same hybrid model is then fit both parametrically and locally to this segmentation. For the hybrid model fit we employ several new variations on the physically-motivated paradigm which seek to speed recovery while guaranteeing stability. A by-product of these variations is an increased generality of the method via the elimination, of some of its ad hoc parameters. We apply our cooperative framework to the recovery of branching cylindrical structures from 3D image volumes. The hybrid model we employ has a novel parametric component which is a fusion of individual cylinders. These cylinders have spines that are arbitrary space curves and cross-sections which may be any star shaped planar curve.
14750394
Global models with parametric offsets as applied to cardiac motion recovery
We introduce a new solid shape model formulation that includes built-in offsets from a base global component (e.g. an ellipsoid) which are functions of the global component's parameters. The offsets provide two features. First, they help to form an expected model shape which facilitates appropriate model data correspondences. Second, they scale with the base global model to maintain the expected shape even in the presence of large global deformations. We apply this model formulation to the recovery of 3-D cardiac motion from a volunteer dataset of tagged-MR images. The model instance is a variation of the hybrid volumetric ventriculoid (HVV), a deformable thick-walled ellipsoid model resembling the left ventricle (LV) of the heart. A unique aspect of of implementation is the employment of constant volume constraints when recovering the cardiac motion. In addition, we present a novel geodesic-like prismoidal tessellation of the model which provides for more stable fits.
3094106
LIPTUS: associating structured and unstructured information in a banking environment
Growing competition has made today's banks understand the value of knowing their customers better. In this paper, we describe a tool, LIPTUS, that associates the customer interactions (emails and transcribed phone calls) with customer and account profiles stored in an existing data warehouse. The associations discovered by LIPTUS enable analytics spanning the customer and account profiles on one hand and the meta-data associated or derived from the interaction (using text mining techniques) on the other. We illustrate the value derived from this consolidated analysis through specific customer intelligence applications. LIPTUS is today being extensively used in a large bank in India. A highlight of this paper is a discussion of the technical challenges encountered while building LIPTUS and deploying it on real-life customer data.
29871849
Optimal Deployment of Triggers for Detecting Events
In active d atabases, rules are represented in the form of ECA (event-condition-action). Database events can be detected by defining triggers on the underlying application databases. Many-a-times, temporal conditions that limit the validity period of the event are as sociated with the ECA rule. The performance of the database can get adversely affected if such temporal constraints are checked (either at the application level or at database level) for every transaction (event) irrespective of whether that transaction (event) has occurred within the said time interval. This drawback can be avoided by optimizing the temporal constraints associated with the sub-events of a composite event based on the semantics of the composite event operators. This paper describes such an algorithm that optimizes the temporal constraints associated with (composite) events and improves the efficiency of the databases by creating and destroying triggers dynamically such that the semantics of the event is unchanged. The efficiency of the technique is validated by our experimental results.
6426062
Understanding Patterns of Technology Evolution: An Ecosystem Perspective
Understanding the dynamics of technology evolution — whether for the purposes of forecasting new product or technology infrastructure developments, or identifying the basis for future digital convergence in the global market— is a key challenge for innovators, senior managers, and policymakers. This research provides an overview of a new ecosystem model of technology evolution, the purpose of which is to structure these kinds of assessments and suggest reusable analysis structures to ensure that total environment of technological innovation is considered. We use examples from the end-user computing context and the electronics industry to identify five patterns of technology evolution that commonly occur. We also develop a state diagram-based approach to demonstrate the cyclical nature of technology evolution. Finally, we illustrate our findings using a case study on digital music technologies.
7914809
Predicting Bidders' Willingness to Pay in Online Multiunit Ascending Auctions: Analytical and Empirical Insights
We develop a real-time estimation approach to predict bidders' maximum willingness to pay in a multiunit ascending uniform-price and discriminatory-price (Yankee) online auction. Our two-stage approach begins with a bidder classification step, which is followed by an analytical prediction model. The classification model identifies bidders as either adopting a myopic best-response (MBR) bidding strategy or a non-MBR strategy. We then use a generalized bid-inversion function to estimate the willingness to pay for MBR bidders. We empirically validate our two-stage approach using data from two popular online auction sites. Our joint classification-and-prediction approach outperforms two other naive prediction strategies that draw random valuations between a bidder's current bid and the known market upper bound. Our prediction results indicate that, on average, our estimates are within 2% of bidders' revealed willingness to pay for Yankee and uniform-price multiunit auctions. We discuss how our results can facilitate mechanism-design changes such as dynamic-bid increments and dynamic buy-it-now prices.
14465668
The economics of network management
Why are pricing issues the key to infrastructure investment and future growth of the Internet? The Economics of network management I t is not surprising that researchers in network technology are utilizing ideas from the field of economics since it provides the conceptual understanding of underlying constructs such as usage and resource allocation. Proper resource allocation plays a key role in improving network performance. There are two primary approaches to economic resource allocation: quantity limits, and pricing. The former has the advantage of low accounting costs. However, it requires central administration and, for network users wishing to purchase more quantity than allocated, a pricing schedule has to be developed. The pricing approach does not enforce any quantity limits. Instead, users self-select the quantity they are willing to purchase at prevailing prices. The pricing approach decentralizes the resource allocation problem but has higher accounting costs since usage needs to be monitored and billed. The role of pricing as a resource allocation mechanism will be examined here, since central administration of quotas would be difficult in a global network. Furthermore, a pricing mechanism may still be needed to meet user demands in e-commerce. While the short-term role of economic pricing is to manage the network resources more efficiently, in the long run it could play an important role in the design of new network protocols. For example, new technological developments with priority classes in the IPv6 Internet protocol [3] will present the challenge of defining an economic basis for priority allocation. The Internet traffic-pricing problem for e-commerce subsumes traditional telecommuni-cation pricing policies and is much more complex. Traditional telecommunication systems offer a single quality level for their services on a given type of network, for example, the ability to carry voice data at a desired rate on a telephone network. However, in e-commerce there is a rich collection of services that require different levels of service quality over the same data communication network. For example, during a telnet session a user requires a high-speed and low-volume data transfer for real-time interaction with a server. On the other hand, a video conference needs clarity and coordination in picture and sound, necessitating a much more complex set of service quality attributes, such as synchronization of information sent via different interoperable applications (video, audio, and text). In addition, audio and video applications require high-volume and high-speed transfers with low variability in data transfer rates. These …