IDENTIFYING AND MONITORING THE ROLES OF CAVITATION IN HEATING FROM HIGH-INTENSITY FOCUSED ULTRASOUND

For high-intensity focused ultrasound (HIFU) to continue to gain acceptance for cancer treatment it is necessary to understand how the applied ultrasound interacts with gas trapped in the tissue. The presence of bubbles in the target location have been thought to be responsible for shielding the incoming pressure and increasing local heat deposition due to the bubble dynamics. We lack adequate tools for monitoring the cavitation process, due to both limited visualization methods and understanding of the underlying physics. The goal of this project was to elucidate the role of inertial cavitation in HIFU exposures in the hope of applying noise diagnostics to monitor cavitation activity and control HIFU-induced cavitation in a beneficial manner. A number of approaches were taken to understand the relationship between inertial cavitation signals, bubble heating, and bubble shielding in agar-graphite tissue phantoms. Passive cavitation detection (PCD) techniques were employed to detect inertial bubble collapses while the temperature was monitored with an embedded thermocouple. Results indicate that the broadband noise amplitude is correlated to bubble-enhanced heating. Monitoring inertial cavitation at multiple positions throughout the focal region demonstrated that bubble activity increased prefocally as it diminished near the focus. Lowering the HIFU duty cycle had the effect of maintaining a more or less constant cavitation signal, suggesting the shielding effect diminished when the bubbles had a chance to dissolve during the HIFU off-time. Modeling the effect of increasing the ambient temperature showed that bubbles do not collapse as violently at higher temperatures due to increased vapor pressure inside the bubble. Our conclusion is that inertial cavitation heating is less effective at higher temperatures and bubble shielding is involved in shifting energy deposition at the focus. The use of a diagnostic ultrasound imaging system as a PCD array was explored. Filtering out the scattered harmonics from the received RF signals resulted in a spatially- resolved inertial cavitation signal, while the amplitude of the harmonics showed a correlation with temperatures approaching the onset of boiling. The result is a new tool for detecting a broader spectrum of bubble activity and thus enhancing HIFU treatment visualization and feedback.

Estimating 3D Hand Pose from a Cluttered Image

A method is proposed that can generate a ranked list of plausible three-dimensional hand configurations that best match an input image. Hand pose estimation is formulated as an image database indexing problem, where the closest matches for an input hand image are retrieved from a large database of synthetic hand images. In contrast to previous approaches, the system can function in the presence of clutter, thanks to two novel clutter-tolerant indexing methods. First, a computationally efficient approximation of the image-to-model chamfer distance is obtained by embedding binary edge images into a high-dimensional Euclide an space. Second, a general-purpose, probabilistic line matching method identifies those line segment correspondences between model and input images that are the least likely to have occurred by chance. The performance of this clutter-tolerant approach is demonstrated in quantitative experiments with hundreds of real hand images.

An Improved Robust Fuzzy Extractor

We consider the problem of building robust fuzzy extractors, which allow two parties holding similar random variables W, W' to agree on a secret key R in the presence of an active adversary. Robust fuzzy extractors were defined by Dodis et al. in Crypto 2006 [6] to be noninteractive, i.e., only one message P, which can be modified by an unbounded adversary, can pass from one party to the other. This allows them to be used by a single party at different points in time (e.g., for key recovery or biometric authentication), but also presents an additional challenge: what if R is used, and thus possibly observed by the adversary, before the adversary has a chance to modify P. Fuzzy extractors secure against such a strong attack are called post-application robust. We construct a fuzzy extractor with post-application robustness that extracts a shared secret key of up to (2m−n)/2 bits (depending on error-tolerance and security parameters), where n is the bit-length and m is the entropy of W . The previously best known result, also of Dodis et al., [6] extracted up to (2m − n)/3 bits (depending on the same parameters).

Spatiotemporal Gesture Segmentation

Spotting patterns of interest in an input signal is a very useful task in many different fields including medicine, bioinformatics, economics, speech recognition and computer vision. Example instances of this problem include spotting an object of interest in an image (e.g., a tumor), a pattern of interest in a time-varying signal (e.g., audio analysis), or an object of interest moving in a specific way (e.g., a human's body gesture). Traditional spotting methods, which are based on Dynamic Time Warping or hidden Markov models, use some variant of dynamic programming to register the pattern and the input while accounting for temporal variation between them. At the same time, those methods often suffer from several shortcomings: they may give meaningless solutions when input observations are unreliable or ambiguous, they require a high complexity search across the whole input signal, and they may give incorrect solutions if some patterns appear as smaller parts within other patterns. In this thesis, we develop a framework that addresses these three problems, and evaluate the framework's performance in spotting and recognizing hand gestures in video. The first contribution is a spatiotemporal matching algorithm that extends the dynamic programming formulation to accommodate multiple candidate hand detections in every video frame. The algorithm finds the best alignment between the gesture model and the input, and simultaneously locates the best candidate hand detection in every frame. This allows for a gesture to be recognized even when the hand location is highly ambiguous. The second contribution is a pruning method that uses model-specific classifiers to reject dynamic programming hypotheses with a poor match between the input and model. Pruning improves the efficiency of the spatiotemporal matching algorithm, and in some cases may improve the recognition accuracy. The pruning classifiers are learned from training data, and cross-validation is used to reduce the chance of overpruning. The third contribution is a subgesture reasoning process that models the fact that some gesture models can falsely match parts of other, longer gestures. By integrating subgesture reasoning the spotting algorithm can avoid the premature detection of a subgesture when the longer gesture is actually being performed. Subgesture relations between pairs of gestures are automatically learned from training data. The performance of the approach is evaluated on two challenging video datasets: hand-signed digits gestured by users wearing short sleeved shirts, in front of a cluttered background, and American Sign Language (ASL) utterances gestured by ASL native signers. The experiments demonstrate that the proposed method is more accurate and efficient than competing approaches. The proposed approach can be generally applied to alignment or search problems with multiple input observations, that use dynamic programming to find a solution.