Shells and Spheres: An n-Dimensional Framework for Medial-Based Image Segmentation

We have developed a method for extracting anatomical shape models from n-dimensional images using an image analysis framework we call Shells and Spheres. This framework utilizes a set of spherical operators centered at each image pixel, grown to reach, but not cross, the nearest object boundary by incorporating “shells” of pixel intensity values while analyzing intensity mean, variance, and first-order moment. Pairs of spheres on opposite sides of putative boundaries are then analyzed to determine boundary reflectance which is used to further constrain sphere size, establishing a consensus as to boundary location. The centers of a subset of spheres identified as medial (touching at least two boundaries) are connected to identify the interior of a particular anatomical structure. For the automated 3D algorithm, the only manual interaction consists of tracing a single contour on a 2D slice to optimize parameters, and identifying an initial point within the target structure

Leveraging The Finite States of Emotion Processing to Study Late-Life Mental Health

Traditional approaches in mental health research apply General Linear Models (GLM) to describe the longitudinal dynamics of observed psycho-behavioral measurements (questionnaire summary scores). Similarly, GLMs are also applied to characterize relationships between neurobiological measurements (regional fMRI signals) and perceptual stimuli or other regional signals. While these methods are useful for exploring linear correlations among the isolated signals of those constructs (i.e., summary scores or fMRI signals), these classical frameworks fall short in providing insights into the comprehensive system-level dynamics underlying observable changes. Hidden Markov Models (HMM) are a statistical model that enable us to describe the sequential relations among multiple observable constructs, and when applied through the lens of Finite State Automata (FSA), can provide a more integrated and intuitive framework for modeling and understanding the underlying controller (the prescription for how to respond to inputs) that fundamentally defines any system, as opposed to linearly correlating output signals produced by the controller. We present a simple and intuitive HMM processing pipeline vcHMM (See Preliminary Data) that highlights FSA theory and is applicable for both behavioral analysis of questionnaire data and fMRI data. HMMs offer theoretic promise as they are computationally equivalent to the FSA, the control processor of a Turing Machine (TM) The dynamic programming Viterbi algorithm is used to leverage the HMM model. It efficiently identifies the most likely sequence of hidden states. The vcHMM pipeline leverages this grammar to understand how behavior and neural activity relate to depression

One-Dimensional Haptic Rendering Using Audio Speaker with Displacement Determined by Inductance

We report overall design considerations and preliminary results for a new haptic rendering device based on an audio loudspeaker. Our application models tissue properties during microsurgery. For example, the device could respond to the tip of a tool by simulating a particular tissue, displaying a desired compressibility and viscosity, giving way as the tissue is disrupted, or exhibiting independent motion, such as that caused by pulsations in blood pressure. Although limited to one degree of freedom and with a relatively small range of displacement compared to other available haptic rendering devices, our design exhibits high bandwidth, low friction, low hysteresis, and low mass. These features are consistent with modeling interactions with delicate tissues during microsurgery. In addition, our haptic rendering device is designed to be simple and inexpensive to manufacture, in part through an innovative method of measuring displacement by existing variations in the speaker’s inductance as the voice coil moves over the permanent magnet. Low latency and jitter are achieved by running the real-time simulation models on a dedicated microprocessor, while maintaining bidirectional communication with a standard laptop computer for user controls and data logging

Time Tomographic Reflection: Phantoms for Calibration and Biopsy

We aim to validate Real Time Tomographic Reflection (RTTR) as an image guidance technique for needle biopsy. RTTR is a new method of in situ visualization, which merges the visual outer surface of a patient with a simultaneous ultrasound scan of the patient’s interior using a half-silvered mirror. The ultrasound image is visually merged with the patient, along with the operator's hands and the invasive tool in the operator’s natural field of view. Geometric relationships are preserved in a single environment, without the tool being restricted to lie in the plane of the ultrasound slice. The present experiment illustrates the effectiveness of needle biopsy using RTTR on a phantom consisting of an olive embedded in a turkey breast and discusses several prototypes of calibration phantoms. 1

Creation and Demonstration of a Framework for Handling Paths

Abstract. A hierarchy of path data types and basic path filters were added to ITK, providing a general framework for curves that map a scalar value to a point in n-dimensional space. The framework supports curves that are either continuous (parametric curves) or discrete (chain-codes). Example usage of the entire framework is demonstrated using a previously published 2D active contour algorithm that was converted to ITK