Accelerated Nonrigid Intensity-Based Image Registration Using Importance Sampling

Nonrigid image registration methods using intensity-based similarity metrics are becoming increasingly common tools to estimate many types of deformations. Nonrigid warps can be very flexible with a large number of parameters and gradient optimization schemes are widely used to estimate them. However, for large datasets, the computation of the gradient of the similarity metric with respect to these many parameters becomes very time consuming. Using a small random subset of image voxels to approximate the gradient can reduce computation time. This work focuses on the use of importance sampling to reduce the variance of this gradient approximation. The proposed importance sampling framework is based on an edge-dependent adaptive sampling distribution designed for use with intensity-based registration algorithms. We compare the performance of registration based on stochastic approximations with and without importance sampling to that using deterministic gradient descent. Empirical results, on simulated magnetic resonance brain data and real computed tomography inhale-exhale lung data from eight subjects, show that a combination of stochastic approximation methods and importance sampling accelerates the registration process while preserving accuracy.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85955/1/Fessler13.pd

Analysis and Strategies to Enhance Intensity-Base Image Registration.

The availability of numerous complementary imaging modalities allows us to obtain a detailed picture of the body and its functioning. To aid diagnostics and surgical planning, all available information can be presented by visually aligning images from different modalities using image registration. This dissertation investigates strategies to improve the performance of image registration algorithms that use intensity-based similarity metrics. Nonrigid warp estimation using intensity-based registration can be very time consuming. We develop a novel framework based on importance sampling and stochastic approximation techniques to accelerate nonrigid registration methods while preserving their accuracy. Registration results for simulated brain MRI data and human lung CT data demonstrate the efficacy of the proposed framework. Functional MRI (fMRI) is used to non-invasively detect brain-activation by acquiring a series of brain images, called a time-series, while the subject performs tasks designed to stimulate parts of the brain. Consequently, these studies are plagued by subject head motion. Mutual information (MI) based slice-to-volume (SV) registration algorithms used to estimate time-series motion are less accurate for end-slices (i.e., slices near the top of the head scans), where a loss in image complexity yields noisy MI estimates. We present a strategy, dubbed SV-JP, to improve SV registration accuracy for time-series end-slices by using joint pdf priors derived from successfully registered high complexity slices near the middle of the head scans to bolster noisy MI estimates. Although fMRI time-series registration can estimate head motion, this motion also spawns extraneous intensity fluctuations called spin saturation artifacts. These artifacts hamper brain-activation detection. We describe spin saturation using mathematical expressions and develop a weighted-average spin saturation (WASS) correction scheme. An algorithm to identify time-series voxels affected by spin saturation and to implement WASS correction is outlined. The performance of registration methods is dependant on the tuning parameters used to implement their similarity metrics. To facilitate finding optimal tuning parameters, we develop a computationally efficient linear approximation of the (co)variance of MI-based registration estimates. However, empirically, our approximation was satisfactory only for a simple mono-modality registration example and broke down for realistic multi-modality registration where the MI metric becomes strongly nonlinear.Ph.D.Electrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/61552/1/rbhagali_1.pd

Biological image analysis

In biological research images are extensively used to monitor growth, dynamics and changes in biological specimen, such as cells or plants. Many of these images are used solely for observation or are manually annotated by an expert. In this dissertation we discuss several methods to automate the annotating and analysis of bio-images. Two large clusters of methods have been investigated and developed. A first set of methods focuses on the automatic delineation of relevant objects in bio-images, such as individual cells in microscopic images. Since these methods should be useful for many different applications, e.g. to detect and delineate different objects (cells, plants, leafs, ...) in different types of images (different types of microscopes, regular colour photographs, ...), the methods should be easy to adjust. Therefore we developed a methodology relying on probability theory, where all required parameters can easily be estimated by a biologist, without requiring any knowledge on the techniques used in the actual software. A second cluster of investigated techniques focuses on the analysis of shapes. By defining new features that describe shapes, we are able to automatically classify shapes, retrieve similar shapes from a database and even analyse how an object deforms through time

Tartu Ülikooli toimetised. Tööd semiootika alalt. 1964-1992. 0259-4668

http://www.ester.ee/record=b1331700*es

1997 international Sherwood fusion theory conference

Papers presented during the conference are indexed separately

Text Segmentation in Web Images Using Colour Perception and Topological Features

The research presented in this thesis addresses the problem of Text Segmentation in Web images. Text is routinely created in image form (headers, banners etc.) on Web pages, as an attempt to overcome the stylistic limitations of HTML. This text however, has a potentially high semantic value in terms of indexing and searching for the corresponding Web pages. As current search engine technology does not allow for text extraction and recognition in images, the text in image form is ignored. Moreover, it is desirable to obtain a uniform representation of all visible text of a Web page (for applications such as voice browsing or automated content analysis). This thesis presents two methods for text segmentation in Web images using colour perception and topological features. The nature of Web images and the implicit problems to text segmentation are described, and a study is performed to assess the magnitude of the problem and establish the need for automated text segmentation methods. Two segmentation methods are subsequently presented: the Split-and-Merge segmentation method and the Fuzzy segmentation method. Although approached in a distinctly different way in each method, the safe assumption that a human being should be able to read the text in any given Web Image is the foundation of both methods’ reasoning. This anthropocentric character of the methods along with the use of topological features of connected components, comprise the underlying working principles of the methods. An approach for classifying the connected components resulting from the segmentation methods as either characters or parts of the background is also presented

Screening and purification of bioactive peptides with potential to activate the cholecystokinin receptor type 1

Obesity is a world-wide health problem with tremendous health care costs. Weight maintenance is a complex system in which different mechanisms are involved. One of these mechanisms involves the cholecystokinin receptor type 1 (CCK1R). The CCK1R is a GPCR (G-protein coupled receptor) localized in the gastrointestinal tract that induces a feeling of satiety upon activation by its natural hormone cholecystokinin (CCK). Bioactive peptides, which can be released from food protein, can mimic the effect of CCK and have an influence on satiety. Such peptides could be used as a satiating ingredient in the development of new functional foods for the prevention and treatment of obesity. We set up a cell-based bioassay in 96 well-plates to screen for such bioactive peptides that can activate the CCK1R, based on the fluorescent visualization of a Ca-flux when the receptor is activated. Fluorescence measurements were done using a plate reader and a confocal microscope and the assay was benchmarked with CCK-8S (sulfated octapeptide), JMV-180 and lorglumide. The confocal microscope appeared to be the preferred measuring device when complex samples had to be measured, as measurements with the plate reader could easily be biased by background fluorescence of the sample. Screening of different food protein hydrolysates showed that some food protein hydrolysates , such as soy protein hydrolysates, possess significant CCK1R activity. The peptides in the active soy protein hydrolysates were separated by use of size exclusion chromatography, the CCK1R activity of the resulting fractions was re-evaluated and significant in vitro CCK1R activity was found. The effect on food intake of the active fractions with a physiological relevant molecular weight was evaluated in vivo with rats, but no significant effect could be measured. The amino acid sequences of the peptides present in some promising fractions was identified, however it remained not possible to identify which particular peptide(s) accounted for the CCK1R activity as more than 100 peptides were still present in the fractions. Hence, a highly-selective tool to extract and identify the active peptides was necessary. Therefore, a first onset was made to incorporate the CCK1R into NABBs (nanoscale apo-lipoprotein bound bilayer particles), a unique native-like bilayer membrane system for incorporation of GPCRs, as such nanoparticles could be used as an affinity-selection-mass spectrometry technique to identify CCK1R-binding peptides. Functional incorporation of the CCK1R in NABBs was shown by binding with a fluorescent labeled CCK analog

Efficient Deep Neural Networks

The success of deep neural networks (DNNs) is attributable to three factors: increased compute capacity, more complex models, and more data. These factors, however, are not always present, especially for edge applications such as autonomous driving, augmented reality, and internet-of-things. Training DNNs requires a large amount of data, which is difficult to obtain. Edge devices such as mobile phones have limited compute capacity, and therefore, require specialized and efficient DNNs. However, due to the enormous design space and prohibitive training costs, designing efficient DNNs for different target devices is challenging. So the question is, with limited data, compute capacity, and model complexity, can we still successfully apply deep neural networks?This dissertation focuses on the above problems and improving the efficiency of deep neural networks at four levels. Model efficiency: we designed neural networks for various computer vision tasks and achieved more than 10x faster speed and lower energy. Data efficiency: we developed an advanced tool that enables 6.2x faster annotation of a LiDAR point cloud. We also leveraged domain adaptation to utilize simulated data, bypassing the need for real data. Hardware efficiency: we co-designed neural networks and hardware accelerators and achieved 11.6x faster inference. Design efficiency: the process of finding the optimal neural networks is time-consuming. Our automated neural architecture search algorithms discovered, using 421x lower computational cost than previous search methods, models with state-of-the-art accuracy and efficiency