Speckle Detection in Ultrasonic Images Using Unsupervised Clustering Techniques

Research for the improvement of the quality of clinical ultrasound images has been a topic of interest for researchers and physicians. One of the challenges is the presence of speckle artifacts. This dissertation reviews the speckle phenomena in such images, and develops algorithms to better identify this artifact in sonographic images. Speckle artifact is categorized into two groups: partially developed speckles and fully developed speckles (FDS). This concept has been used, along with the classification techniques, to segment the ultrasound images into patches and classify the patches in the image as FDS or non-FDS. The proposed algorithms and the results of the experiments have been validated using simulation, phantom and real data that were created for the purposes of this study or taken from other research groups. Current speckle detection methods do not optimize statistical features and they are not based on machine learning techniques. For the first time this work introduces a novel method for searching and extracting the best features for optimizing speckle detection rate using statistical machine learning and ensemble classification. Potential applications include strain imaging by tracking speckle displacement, elastography, speckle tracking and suppression applications, and needle-tracking applications.Ph.D., Biomedical Engineering -- Drexel University, 201

Echocardiography

The book "Echocardiography - New Techniques" brings worldwide contributions from highly acclaimed clinical and imaging science investigators, and representatives from academic medical centers. Each chapter is designed and written to be accessible to those with a basic knowledge of echocardiography. Additionally, the chapters are meant to be stimulating and educational to the experts and investigators in the field of echocardiography. This book is aimed primarily at cardiology fellows on their basic echocardiography rotation, fellows in general internal medicine, radiology and emergency medicine, and experts in the arena of echocardiography. Over the last few decades, the rate of technological advancements has developed dramatically, resulting in new techniques and improved echocardiographic imaging. The authors of this book focused on presenting the most advanced techniques useful in today's research and in daily clinical practice. These advanced techniques are utilized in the detection of different cardiac pathologies in patients, in contributing to their clinical decision, as well as follow-up and outcome predictions. In addition to the advanced techniques covered, this book expounds upon several special pathologies with respect to the functions of echocardiography

Superpixel lattices

Superpixels are small image segments that are used in popular approaches to object detection and recognition problems. The superpixel approach is motivated by the observation that pixels within small image segments can usually be attributed the same label. This allows a superpixel representation to produce discriminative features based on data dependent regions of support. The reduced set of image primitives produced by superpixels can also be exploited to improve the efficiency of subsequent processing steps. However, it is common for the superpixel representation to have a different graph structure from the original pixel representation of the image. The first part of the thesis argues that a number of desirable properties of the pixel representation should be maintained by superpixels and that this is not possible with existing methods. We propose a new representation, the superpixel lattice, and demonstrate its advantages. The second part of the thesis investigates incorporating a priori information into superpixel segmentations. We learn a probabilistic model that describes the spatial density of object boundaries in the image. We demonstrate our approach using road scene data and show that our algorithm successfully exploits the spatial distribution of object boundaries to improve the superpixel segmentation. The third part of the thesis presents a globally optimal solution to our superpixel lattice problem in either the horizontal or vertical direction. The solution makes use of a Markov Random Field formulation where the label field is guaranteed to be a set of ordered layers. We introduce an iterative algorithm that uses this framework to learn colour distributions across an image in an unsupervised manner. We conclude that our approach achieves comparable or better performance than competing methods and that it confers several additional advantages

The transcriptome response of leaves of the resurrection plant, Xerophyta humilis to desiccation

Includes bibliographical references.In angiosperms, desiccation tolerance, a genetic trait that enables tissues to survive loss of more than 95% of cellular water is widely observed in the seeds, but is only found in the vegetative tissues of a small group of species known as the resurrection plants. Xerophyta humilis is a small resurrection plant indigenous to Southern Africa. In this study, the hypothesis that vegetative desiccation tolerance is derived from an adaptation of seed desiccation tolerance was tested by characterizing changes in the transcriptome of X. humilis leaves during desiccation. The mRNA transcript abundance of a set of 1680 X. humilis genes was analyzed at 6 different stages of water loss in the leaves of X. humilis. Functional enrichment analysis showed that genes that were down-regulated during desiccation were over-represented with genes involved in photosynthesis, cellular developmental processes, as well as transcription regulator activity. Three distinct clusters of up-regulated genes were identified. The earliest set of up-regulated genes were enriched with genes associated with the turnover of proteins and the simultaneous synthesis of proteins required for protection. Enrichment also included genes associated with lipid body synthesis, as well as the transport of storage proteins to vacuoles. Two groups of late desiccation up-regulated genes were also identified, their expression only increased at later stages of desiccation and remained high in the desiccated leaves

Self-adaptation and rule generation in a fuzzy system for X-ray rocking curve analysis.

X-ray rocking curve analysis is an example of a changing application domain. The salient characteristic of such a domain is that situations and facts can change over time. This means that the domain cannot be modelled by a fixed set of fuzzy rules. Instead, the rules must change over time and these changes must model actual changes that occur in the application domain. Three new techniques have been developed for altering a set of fuzzy rules: altering the credibility weight of an expert and using connection matrices to shift the focus of attention between different sets of rules; fine-tuning and changing the membership functions of fuzzy premise variables and thereby altering the meaning of the rules; and generating new fuzzy rules by inductive learning from examples. A fuzzy system for X -ray rocking curve analysis has been developed and used to test each of these techniques. This fuzzy system uses frames, logic-based variables, connection matrices and credibility weights, fuzzy rules and a record of previous decisions in order to model X-ray rocking curve analysis. Question and answer sessions with the user are used to describe experimental rocking curves and structural parameters are deduced from this description. These structural parameters are then used to simulate a theoretical curve, which is compared with the experimental one. A performance measure is derived to calculate the degree of matching between the two curves. This performance measure is used to test each of the three techniques in turn. Tests have shown that the fuzzy system optimises its performance to suit new situations and facts

In Silico Methodologies for Selection and Prioritization of Compounds in Drug Discovery

Ph.DDOCTOR OF PHILOSOPH

Neuroinformatics in Functional Neuroimaging

This Ph.D. thesis proposes methods for information retrieval in functional neuroimaging through automatic computerized authority identification, and searching and cleaning in a neuroscience database. Authorities are found through cocitation analysis of the citation pattern among scientific articles. Based on data from a single scientific journal it is shown that multivariate analyses are able to determine group structure that is interpretable as particular “known ” subgroups in functional neuroimaging. Methods for text analysis are suggested that use a combination of content and links, in the form of the terms in scientific documents and scientific citations, respectively. These included context sensitive author ranking and automatic labeling of axes and groups in connection with multivariate analyses of link data. Talairach foci from the BrainMap ™ database are modeled with conditional probability density models useful for exploratory functional volumes modeling. A further application is shown with conditional outlier detection where abnormal entries in the BrainMap ™ database are spotted using kernel density modeling and the redundancy between anatomical labels and spatial Talairach coordinates. This represents a combination of simple term and spatial modeling. The specific outliers that were found in the BrainMap ™ database constituted among others: Entry errors, errors in the article and unusual terminology

Glycosylation of immunoglobulin G in cerebrospinal fluid and multiple sclerosis.

The glycosylation features of CSF oligoclonal IgG, and possible changes in N-glycans of CSF IgG in multiple sclerosis (MS) were studied. After isoelectric focusing (IEF) of CSF, bands were detected using biotinylated lectins and avidin-horseradish peroxidase. Concanavalin A (Con A) binding showed that mannose exists throughout the pH range of oligoclonal IgG. Sambucus nigra antigen (SNA) bound acidic and neutral oligoclonal IgG only, suggesting that alkaline oligoclonal IgG is deficient in sialic acid. Deglycosylation of CSF IgG using peptide-N-glycosidase F suggested that the range of isoelectric points of oligoclonal IgG bands is not due to carbohydrate differences alone. Lectin immunoassays, whereby protein A purified IgG was captured by anti-IgG coated tubes and probed using a range of biotinylated lectins, were used to compare 13 CSF samples from MS patients with 14 control samples. With Con A binding, a significantly higher mean and larger variance was found for the MS group (t-test: P<0.05). Con A binding correlated with CSF [IgG]/[total protein]% (r=0.390; P=0.0443). Using HPLC to separate oligosaccharides released from IgG by hydrazinolysis and labelled with 2-aminobenzamide, glycans were determined in 7 CSF samples with oligoclonal IgG, and 6 CSF samples without. The ratio of the peak for biantennary fucosylated agalactosyl glycans to total monogalactosylated glycan peaks was lower for the oligoclonal IgG samples (t-test: P=0.0141). The overall results suggested that glycosylation changes occur in CSF IgG in MS, and that oligoclonal IgG contains less sialic acid but more galactose than polyclonal IgG