Kalpathi R. Subramanian

Applying space subdivision techniques to volume rendering

The authors present a ray-tracing algorithm for volume rendering designed to work efficiently when the data of interest is distributed sparsely through the volume. A simple preprocessing step identifies the voxels representing features of interest. Frequently this set of voxels, arbitrarily distributed in three-dimensional space, is a small fraction of the original voxel grid. A median-cut space partitioning scheme, combined with bounding volumes to prune void spaces in the resulting search structure, is used to store the voxels of interest in a k-d tree. The k-d tree is used as a data structure. The tree is then efficiently ray-traced to render the voxel data. The k-d tree is view independent, and can be used for animation sequences involving changes in positions of the viewer or positions of lights. This search structure has been applied to render voxel data from MRI, CAT scan, and electron density distributions.<<ETX>>

Active contours using a constraint-based implicit representation

We present a new constraint-based implicit active contour, which shares desirable properties of both parametric and implicit active contours. Like parametric approaches, their representation is compact and can be manipulated interactively. Like other implicit approaches, they can naturally adapt to nonsimple topologies. Unlike implicit approaches using level-set methods, representation of the contour does not require a dense mesh. Instead, it is based on specified on-curve and off-curve constraints, which are interpolated using radial basis functions. These constraints are evolved according to specified forces drawn from the relevant literature of both parametric and implicit approaches. This new type of active contour is demonstrated through synthetic images, photographs, and medical images with both simple and nonsimple topologies. For complex input, this approach produces results comparable to those of level set or parameterized finite-element active models, but with a compact analytic representation. As with other active contours they can also be used for tracking, especially for multiple objects that split or merge.

An optical flow approach to tracking colonoscopy video

We can supplement the clinical value of an optical colonoscopy procedure if we can continuously co-align corresponding virtual colonoscopy (from preoperative X-ray CT exam) and optical colonoscopy images. In this work, we demonstrate a computer vision algorithm based on optical flow to compute egomotion from live colonoscopy video, which is then used to navigate and visualize the corresponding patient anatomy from X-ray CT data. The key feature of the algorithm lies in the effective combination of sparse and dense optical flow fields to compute the focus of expansion (FOE); FOE permits independent computation of camera translational and rotational parameters, directly contributing to the algorithms accuracy and robustness. We performed extensive evaluation via a colon phantom and clinical colonoscopy data. We constructed two colon like phantoms, a straight phantom and a curved phantom to measure actual colonoscopy motion; tracking accuracy was quantitatively evaluated by comparing estimated motion parameters (velocity and displacement) to ground truth. Thirty straight and curved phantom sequences were collected at 10, 15 and 20 mm/s (5 trials at each speed), to simulate typical velocities during colonoscopy procedures. The average error in velocity estimation was within 3 mm/s in both straight and curved phantoms. Displacement error was under 7 mm over a total distance of 287-288 mm in the straight and curved phantoms. Algorithm robustness was successfully demonstrated on 27 optical colonoscopy image sequences from 20 different patients, and spanning 5 different colon segments. Specific sequences among these were chosen to illustrate the algorithms decreased sensitivity to (1) recording interruptions, (2) errors in colon segmentation, (3) illumination artifacts, (4) presence of fluid, and (5) changes in colon structure, such as deformation, polyp removal, and surgical tool movement during a procedure.

Development of a Wireless Sensor Network for Monitoring a Bioreactor Landfill

Recent studies of aerobic bioreactors have demonstrated their success in expediting stabilization of municipa l solid waste (MSW), reducing or eliminating treatment and disposal costs of leachate, and increasing landfill capacity. Such aerobic decomposition is highly dependent on maintaining optimum distribution of moisture and air throughout the highly heterogene ous waste for the duration of the stabilization process. This requires distributed monitoring of the temperature and moisture in the bioreactor. This work presents the development and implementation of an autonomous monitoring system using an array of wire less sensors (motes). Each mote is equipped with embedded microprocessor, flash memory, and a wireless transceiver. Networked data collection along with 3D interactive visualization tools are developed for efficient assessment of the conditions in the bior eactor.

Converting discrete images to partitioning trees

The discrete space representation of most scientific datasets, generated through instruments or by sampling continuously defined fields, while being simple, is also verbose and structureless. We propose the use of a particular spatial structure, the binary space partitioning tree as a new representation to perform efficient geometric computation in discretely defined domains. The ease of performing affine transformations, set operations between objects, and correct implementation of transparency makes the partitioning tree a good candidate for probing and analyzing medical reconstructions, in such applications as surgery planning and prostheses design. The multiresolution characteristics of the representation can be exploited to perform such operations at interactive rates by smooth variation of the amount of geometry. Application to ultrasound data segmentation and visualization is proposed. The paper describes methods for constructing partitioning trees from a discrete image/volume data set. Discrete space operators developed for edge detection are used to locate discontinuities in the image from which lines/planes containing the discontinuities are fitted by using either the Hough transform or a hyperplane sort. A multiresolution representation can be generated by ordering the choice of hyperplanes by the magnitude of the discontinuities. Various approximations can be obtained by pruning the tree according to an error metric. The segmentation of the image into edgeless regions can yield significant data compression. A hierarchical encoding schema for both lossless and lossy encodings is described.

A stable optic-flow based method for tracking colonoscopy images

In this paper, we focus on the robustness and stability of our algorithm to plot the position of an endoscopic camera (during a colonoscopy procedure) on the corresponding pre-operative CT scan of the patient. The colon has few topological landmarks, in contrast to bronchoscopy images, where a number of registration algorithms have taken advantage of features such as anatomical marks or bifurcations. Our method estimates the camera motion from the optic-flow computed from the information contained in the video stream. Optic-flow computation is notoriously susceptible to errors in estimating the motion field. Our method relies on the following features to counter this, (1) we use a small but reliable set of feature points (sparse optic-flow field) to determine the spatio-temporal scale at which to perform optic-flow computation in each frame of the sequence, (2) the chosen scales are used to compute a more accurate dense optic flow field, which is used to compute qualitative parameters relating to the main motion direction, and (3) the sparse optic-flow field and the main motion parameters are then combined to estimate the camera parameters. A mathematical analysis of our algorithm is presented to illustrate the stability of our method, as well as comparison to existing motion estimation algorithms. We present preliminary results of using this algorithm on both a virtual colonoscopy image sequence, as well as a colon phantom image sequence.

Visualization of real-time survivability metrics for mobile networks

In this work12 we suggest real-time cost and performance metrics that are useful for survivability analysis at the radio layer of mobile networks. We demonstrate implementation of two of these metrics, to monitor blocking rates for adaptive admission control (AAC), using our simulation framework, to which we have added a data visualization (DV) layer. The DV layer uses 3-D animation to illustrate the effects of parameter selection on AAC performance, where the parameters of interest are those which control measurement and use of real-time metrics. Examples highlight the usefulness of the metrics to manage the tradeoff between new-call and handover channel requests and the capability of the DV layer to represent the spatial and temporal behavior of the AAC algorithm in a comprehensible form.

Interactive analysis and visualization of situationally aware building evacuations

Evacuation of large urban structures, such as campus buildings, arenas, or stadiums, is of prime interest to emergency responders and planners. Although there is a large body of work on evacuation algorithms and their application, most of these methods are impractical to use in real-world scenarios (nonreal-time, for instance) or have difficulty handling scenarios with dynamically changing conditions. Our overall goal in this work is toward developing computer visualizations and real-time visual analytic tools for evacuations of large groups of buildings, and in the long term, integrate this with the street networks in the surrounding areas. A key aspect of our system is to provide situational awareness and decision support to first responders and emergency planners. In our earlier work, we demonstrated an evacuation system that employed a modified variant of a heuristic-based evacuation algorithm, which (1) facilitated real-time complex user interaction with first responder teams, in response to information received during the emergency; (2) automatically supported visual reporting tools for spatial occupancy, temporal cues, and procedural recommendations; and (3) multi-scale building models, heuristic evacuation models, and unique graph manipulation techniques for producing near real-time situational awareness. The system was tested in collaboration with our campus police and safety personnel, via a tabletop exercise consisting of three different scenarios. In this work, we have redesigned the system to be able to handle larger groups of buildings, in order to move toward a full-campus evacuation system. We demonstrate an evacuation simulation involving 22 buildings in the University of North Carolina, Charlotte campus. Second, the implementation has been redesigned as a WebGL application, facilitating easy dissemination and use by stakeholders.

Anatomic modeling from unstructured samples using variational implicit surfaces

We describe the use of variational implicit surfaces (level sets of an embedded generating function modeled using radial basis interpolants) in anatomic modeling. This technique allows the practitioner to employ sparsely and unevenly sampled data to represent complex biological surfaces, including data acquired as a series of non-parallel image slices. The method inherently accommodates interpolation across irregular spans. In addition, shapes with arbitrary topology are easily represented without interpolation or aliasing errors arising from discrete sampling. To demonstrate the medical use of variational implicit surfaces, we present the reconstruction of the inner surfaces of blood vessels from a series of endovascular ultrasound images.

Visual analysis of situationally aware building evacuations

Rapid evacuation of large urban structures (campus buildings, arenas, stadiums, etc.) is a complex operation and of prime interest to emergency responders and planners. Although there is a considerable body of work in evacuation algorithms and methods, most of these are impractical to use in real-world scenarios (non real-time, for instance) or have difficulty handling scenarios with dynamically changing conditions. Our goal in this work is towards developing computer visualizations and real-time visual analytic tools for building evacuations, in order to provide situational awareness and decision support to first responders and emergency planners. We have augmented traditional evacuation algorithms in the following important ways, (1) facilitate real-time complex user interaction with first responder teams, as information is received during an emergency, (2) visual reporting tools for spatial occupancy, temporal cues, and procedural recommendations are provided automatically and at adjustable levels, and (3) multi-scale building models, heuristic evacuation models, and unique graph manipulation techniques for producing near real-time situational awareness. We describe our system, methods and their application using campus buildings as an example. We also report the results of evaluating our system in collaboration with our campus police and safety personnel, via a table-top exercise consisting of 3 different scenarios, and their resulting assessment of the system.