Harmonic Imaging Using a Mechanical Sector, B-MODE

An ultrasound imaging system includes transmitting ultrasound waves into a human body, collecting the reflections, manipulating the reflections, and then displaying them on computer screen as a grayscale image. The standard approach for ultrasound imaging is to use the fundamental frequency from the reflected signal to form images. However, it has been shown that images generated using the harmonic content have improved resolution as well as reduced noise, resulting in clearer images. Although harmonic imaging has been shown to return improved images, this has never been shown with a B-mode, mechanical sector ultrasound system. In this thesis, we demonstrated such a system. First there is a discussion of the theory of harmonic imaging, then a description of the ultrasound system used, and finally experimental results

Combining crowd worker, algorithm, and expert efforts to find boundaries of objects in images

While traditional approaches to image analysis have typically relied upon either manual annotation by experts or purely-algorithmic approaches, the rise of crowdsourcing now provides a new source of human labor to create training data or perform computations at run-time. Given this richer design space, how should we utilize algorithms, crowds, and experts to better annotate images? To answer this question for the important task of finding the boundaries of objects or regions in images, I focus on image segmentation, an important precursor to solving a variety of fundamental image analysis problems, including recognition, classification, tracking, registration, retrieval, and 3D visualization. The first part of the work includes a detailed analysis of the relative strengths and weaknesses of three different approaches to demarcate object boundaries in images: by experts, by crowdsourced laymen, and by automated computer vision algorithms. The second part of the work describes three hybrid system designs that integrate computer vision algorithms and crowdsourced laymen to demarcate boundaries in images. Experiments revealed that hybrid system designs yielded more accurate results than relying on algorithms or crowd workers alone and could yield segmentations that are indistinguishable from those created by biomedical experts. To encourage community-wide effort to continue working on developing methods and systems for image-based studies which can have real and measurable impact that benefit society at large, datasets and code are publicly-shared (http://www.cs.bu.edu/~betke/BiomedicalImageSegmentation/)

Combining crowd worker, algorithm, and expert efforts to find boundaries of objects in images

While traditional approaches to image analysis have typically relied upon either manual annotation by experts or purely-algorithmic approaches, the rise of crowdsourcing now provides a new source of human labor to create training data or perform computations at run-time. Given this richer design space, how should we utilize algorithms, crowds, and experts to better annotate images? To answer this question for the important task of finding the boundaries of objects or regions in images, I focus on image segmentation, an important precursor to solving a variety of fundamental image analysis problems, including recognition, classification, tracking, registration, retrieval, and 3D visualization. The first part of the work includes a detailed analysis of the relative strengths and weaknesses of three different approaches to demarcate object boundaries in images: by experts, by crowdsourced laymen, and by automated computer vision algorithms. The second part of the work describes three hybrid system designs that integrate computer vision algorithms and crowdsourced laymen to demarcate boundaries in images. Experiments revealed that hybrid system designs yielded more accurate results than relying on algorithms or crowd workers alone and could yield segmentations that are indistinguishable from those created by biomedical experts. To encourage community-wide effort to continue working on developing methods and systems for image-based studies which can have real and measurable impact that benefit society at large, datasets and code are publicly-shared (http://www.cs.bu.edu/~betke/BiomedicalImageSegmentation/)

How to collect high quality segmentations: use human or computer drawn object boundaries?

High quality segmentations must be captured consistently for applications such as biomedical image analysis. While human drawn segmentations are often collected because they provide a consistent level of quality, computer drawn segmentations can be collected efficiently and inexpensively. In this paper, we examine how to leverage available human and computer resources to consistently create high quality segmentations. We propose a quality control methodology. We demonstrate how to apply this approach using crowdsourced and domain expert votes for the "best" segmentation from a collection of human and computer drawn segmentations for 70 objects from a public dataset and 274 objects from biomedical images. We publicly share the library of biomedical images which includes 1,879 manual annotations of the boundaries of 274 objects. We found for the 344 objects that no single segmentation source was preferred and that human annotations are not always preferred over computer annotations. These results motivated us to examine the traditional approach to evaluate segmentation algorithms, which involves comparing the segmentations produced by the algorithms to manual annotations on benchmark datasets. We found that algorithm benchmarking results change when the comparison is made to consensus-voted segmentations. Our results led us to suggest a new segmentation approach that uses machine learning to predict the optimal segmentation source and a modified segmentation evaluation approach.National Science Foundation (IIS-0910908

Two-Way Visibly Pushdown Automata and Transducers

Automata-logic connections are pillars of the theory of regular languages. Such connections are harder to obtain for transducers, but important results have been obtained recently for word-to-word transformations, showing that the three following models are equivalent: deterministic two-way transducers, monadic second-order (MSO) transducers, and deterministic one-way automata equipped with a finite number of registers. Nested words are words with a nesting structure, allowing to model unranked trees as their depth-first-search linearisations. In this paper, we consider transformations from nested words to words, allowing in particular to produce unranked trees if output words have a nesting structure. The model of visibly pushdown transducers allows to describe such transformations, and we propose a simple deterministic extension of this model with two-way moves that has the following properties: i) it is a simple computational model, that naturally has a good evaluation complexity; ii) it is expressive: it subsumes nested word-to-word MSO transducers, and the exact expressiveness of MSO transducers is recovered using a simple syntactic restriction; iii) it has good algorithmic/closure properties: the model is closed under composition with a unambiguous one-way letter-to-letter transducer which gives closure under regular look-around, and has a decidable equivalence problem

The Feeling of Color: A Haptic Feedback Device for the Visually Disabled

Tapson J, Gurari N, Diaz J, et al. The Feeling of Color: A Haptic Feedback Device for the Visually Disabled. Presented at the Biomedical Circuits and Systems Conference (BIOCAS), Baltimore, MD.We describe a sensory augmentation system designed to provide the visually disabled with a sense of color. Our system consists of a glove with short-range optical color sensors mounted on its fingertips, and a torso-worn belt on which tactors (haptic feedback actuators) are mounted. Each fingertip sensor detects the observed objectpsilas color. This information is encoded to the tactor through vibrations in respective locations and varying modulations. Early results suggest that detection of primary colors is possible with near 100% accuracy and moderate latency, with a minimum amount of training