Signal detection plays a critical role in realizing reliable transmission through communication systems. In this dissertation, by applying information theoretic approach, efficient detection schemes and algorithms are designed for three particular communication systems. First, a computation efficient coding and detection algorithm is developed to decode two dimensional inter-symbol interference (ISI) channels. The detection algorithm significantly reduces the computation complexity and makes the proposed equalization algorithm applicable. A new metric, the post-detection mutual information (PMI), is established to quantify the ultimate information rate between the discrete inputs and the hard detected output. This is the first time that the information rate loss caused by the hard mapping of the detectors is considered. Since the hard mapping step in the detector is irreversible, we expect that the PMI is reduced compared to the MI without hard mapping. The conclusion is confirmed by both the simulation and the theoretic results. Random complex field code is designed to achieve the secrecy capacity of wiretap channel with noiseless main channel and binary erasure eavesdroppers' channel. More importantly, in addition to approaching the secrecy capacity, RCFC is the first code design which provides a platform to tradeoff secrecy performance with the erasure rate of the eavesdropper's channel and the secrecy rate.

<p>Recent methods to infer genetic networks are based on identifying gene interactions by similarities in expression profiles. These methods are founded on the assumption that interacting genes share higher similarities in their expression profiles than non-interacting genes. In this dissertation this assumption is validated when using mutual information as a similarity measure. Three algorithms that calculate mutual information between expression data are developed: 1) a basic approach implemented with the histogram technique; 2) an extension of the basic approach that takes into consideration time delay between expression profiles; 3) an extension of the basic approach that takes into consideration that genes are regulated in a complex manner by multiple genes. In our experiments we compare the mutual information distributions for profiles of interacting and non-interacting genes. The results show that interacting genes do not share higher mutual information in their expression profiles than non-interacting genes, thus contradicting the basic assumption that similarity measures need to fulfil. This indicates that mutual information is not appropriate as similarity measure, which contradicts earlier proposals.</p>

Influenza A is a negative stranded RNA virus with a segmented genome. Once the virus infects a cell it must replicate its full length viral genomic RNA (vRNA) through a positive sense complementary intermediate RNA (cRNA) as well as transcribe viral messenger RNA (mRNA) using the vRNA as a template. The regulation of whether the viral polymerase replicates the genome by synthesizing cRNA, or produces mRNA in order to make viral protein involves, the viral nucleoprotein (NP). We tried to find the sequence residues of NP that directly interact with the viral polymerase. We mutated to alanine several residues on NP that are surface exposed on recently solved crystal structures as well as those thought to be oriented toward the viral polymerase complex in cryo-EM studies. As a first screen, we tested these mutants in a mini-genome assay where the NP stimulation of the viral polymerase can be studied in transfected cells. Through this screen we found that the NP mutants that hindered its ability to stimulate polymerase activity the most were located in a loop between two alpha helixes in the head domain of NP located at residues 203 to 209. Specifically, the NP single mutants of R204, W207, and R208 were inactive in the mini-genome assay. Using RT-PCR we found that the cRNA to vRNA step of replication is severely inhibited by these mutations. Immunoprecipitation using transfected cells showed that the NP mutants lost the ability to bind all three polymerase subunits. This indicates that this loss of polymerase binding may be the reason the NP mutant fails to stimulate polymerase activity. To make sure that this loss of polymerase stimulation was not due to altering other functions of NP we made sure that the protein had proper cellular localization, oligomerization, and RNA binding abilities. Using immuniflourescence we found that mutant NP localized to the nucleus just like wild type. In order to test RNA binding and oligomerization we tested NP purified from a baculovirus expressing system. Using fluorescence polarization we found that NP binds single stranded RNA with similar affinity to wild type. Using gel filtration we found that mutant NP forms oligomers just like wild type. Using covariation analysis of how different positions in an amino acid alignment change relative to each other we predicted possible binding sites between NP and the three polymerase subunits PA, PB1 and PB2. Due to more complete crystal structure data we focused on the PA-NP interaction and found that covariation aided in finding binding sequence residues on PA but not NP. Another outcome of developing the covariation method was developing a program to view broad primary structure changes in large sequence alignments. This method has been informative in evaluating how amino acid positions in influenza have changed over time, as well as what defines specific residues as belonging to human or avian viruses. / text

In this work were studied, implemented and evaluated two algorithms of image registration with two similarity metrics of mutual information. These were Viola-Wells Mutual Information [6],[7] and Mattes Mutual Information[11]. Materials and Methods: Two 3D MRI T1 and Τ2 brain images were used. The T1 image was rotated in all three axes , with the 27 possible triples of angles 0.25, 1.5 and 3 degrees and in the T2 image were added 3 Gaussian Noise Levels (1,3,5%). Thus were formed two experiments. The monomodal experiment which was registering the initial T1 image with its 27 rotated instances and the multimodal experiment which was registering the 4 T2 images (0,1,3,5% Gaussian Noise) with the 27 rotated T1 images. The registration framework had also a Regular Step Gradient Descent Optimizer, affine linear transformation and linear interpolator. After the 5 experimental set were registered with both algorithms, then in order for the results to be evaluated, 5 similarity metrics were used. These were: 1) Mean Square Difference 2) Correlation Coefficient 3) Joint Entropy 4) Normalized Mutual Information και 5) Entropy of the Difference Image. Finally t-test was applied, in order to find statistically significant differences. Results: Both algorithms had similar outcome, although the algorithm with Mattes Μutual Information metric, had a slightly improved performance. Statistically important differences were found in the t-test. Conclusions: The two methods should be tested more, using other kinds of transformation, and more data sets. / Σε αυτήν την εργασία μελετήθηκαν, υλοποιήθηκαν και αξιολογήθηκαν δύο αλγόριθμοι αντιστοίχισης ιατρικών εικόνων με δύο μετρικές ομοιότητας με χρήση κοινού πληροφορίας. Συγκεκριμένα η υλοποίηση Viola-Wells [6],[7] και η υλοποίηση Mattes[11]. Υλικά και Μέθοδος: Χρησιμοποιήθηκαν δύο εικόνες 3D MRI T1 και Τ2 που απεικόνιζαν εγκέφαλου. Η εικόνα Τ1 περιστράφηκε με τους 27 δυνατές συνδυασμούς των γωνιών 0.25,1.5,3 μοιρών , σε όλους τους άξονες και στην εικόνα Τ2 προστέθηκαν 3 επίπεδα Gaussian θορύβου (1,3,5%). Έτσι σχηματίστηκαν δύο πειράματα. Το μονο-απεικονιστικό πείραμα (Monomodal) που αντιστοιχούσε την αρχική Τ1 εικόνα με τα 27 περιστρεμμένα στιγμιότυπα της και το πολύ-απεικονιστικό (multimodal) που αντιστοιχούσε τις 4 Τ2 εικόνες (0,1,3,5% Gaussian Noise) με τα 27 περιστρεμμένα στιγμιότυπα της Τ1. Το σχήμα της αντιστοίχισης αποτελούνταν εκτός από τις δύο μετρικές ομοιότητας, από τον Regular Step Gradient Descent βελτιστοποιητή , συσχετισμένο (affine) γραμμικό μετασχηματισμό και γραμμικό interpolator. Αφού τα 5 σύνολα πειραμάτων ταυτίστηκαν και με τους 2 αλγορίθμους στην συνέχεια και προκειμένου να αξιολογηθεί το αποτέλεσμα της αντιστοίχισης, χρησιμοποιήθηκαν 5 μετρικές ομοιότητας. Αυτές ήταν : 1) Mean Square Difference 2) Correlation Coefficient 3) Joint Entropy 4) Normalized Mutual Information και 5) Entropy of the Difference Image. Τέλος εφαρμόστηκε και t-test προκειμένου να επιβεβαιωθούν στατιστικώς σημαντικές διαφορές. Αποτελέσματα: Και οι δύο αλγόριθμοι βρέθηκαν να έχουν παρόμοια συμπεριφορά, ωστόσο ο αλγόριθμος που χρησιμοποιούσε την Mattes Μutual Information μετρική ομοιότητας είχε καλύτερα αποτελέσματα. Στατιστικώς σημαντικές διαφορές επιβεβαιώθηκαν και από το t-test. Συμπέρασμα: Οι δύο μέθοδοι θα πρέπει να αξιολογηθούν χρησιμοποιώντας και άλλους μετασχηματισμούς, καθώς και διαφορετικά data set.

Sub-pixel image alignment estimation is desirable for co-registration of objects in multiple images to a common spatial reference and as alignment input to multi-image processing. Applications include super-resolution, image fusion, change detection, object tracking, object recognition, video motion tracking, and forensics.Information theoretical measures are commonly used for co-registration in medical imaging. The published methods apply Shannon's Entropy to the Joint Measurement Space (JMS) of two images. This work introduces into the same context a new set of statistical divergence measures derived from Fisher Information. The new methods described in this work are applicable to uncorrelated imagery and imagery that becomes statistically least dependent upon co-alignment. Both characteristics occur with multi-modal imagery and cause cross-correlation methods, as well as maximum dependence indicators, to fail. Fisher Information-based estimators, together as a set with an Entropic estimator, provide substantially independent information about alignment. This increases the statistical degrees of freedom, allowing for precision improvement and for reduced estimator failure rates compared to Entropic estimator performance alone.The new Fisher Information methods are tested for performance on real remotely-sensed imagery that includes Landsat TM multispectral imagery and ESR SAR imagery, as well as randomly generated synthetic imagery. On real imagery, the co-registration cost function is qualitatively examined for features that reveal the correct point of alignment. The alignment estimates agree with manual alignment to within manual alignment precision. Alignment truth in synthetic imagery is used to quantitatively evaluate co-registration accuracy. The results from the new Fisher Information-based algorithms are compared to Entropy-based Mutual Information and correlation methods revealing equal or superior precision and lower failure rate at signal-to-noise ratios below one.

Prostate cancer continues to be the most commonly diagnosed cancer among men. Brachytherapy has emerged as one of the definitive treatment options for early stage prostate cancer which entails permanent implantation of radioactive seeds into the prostate to eradicate the cancer with ionizing radiation. Successful brachytherapy requires the ability to perform dosimetry -which requires seed localization- during the procedure but such function is not available today. If dosimetry could be performed intraoperatively, physicians could implant additional seeds into the under-dosed portions of the prostate while the patient is still on the operating table. This thesis addresses the brachytherapy seed localization problem with introducing intensity based registration between transrectal ultrasound (TRUS) that shows only the prostate and a 3D seed model drawn from fluoroscopy that shows only the implanted seeds. The TRUS images are first filtered and compounded, and then registered to the seed model by using mutual information. A training phantom was implanted with 48 seeds and imaged. Various ultrasound filtering techniques were analyzed. The effect of false positives and false negatives in ultrasound was investigated by randomly masking seeds from the fluoroscopy volume or adding seeds to that in random locations. Furthermore, the effect of sparse and dense ultrasound data was analyzed by running the registration for ultrasound data with different spacing. The registration error remained consistently below clinical threshold and capture range was significantly larger than the initial guess guaranteed by the clinical workflow. This fully automated method provided excellent registration accuracy and robustness in phantom studies and promises to demonstrate clinically adequate performance on human data. / Thesis (Master, Electrical & Computer Engineering) -- Queen's University, 2008-09-27 12:35:16.691

During orthopaedic surgery, preoperative CT scans can be aligned to the patient to assist the guidance of surgical instruments and the placement of implants. Registration (i.e. alignment) can be accomplished in many ways: by registering implanted fiducial markers, by touching a probe to the bone surface, or by aligning intraoperative two dimensional flouro images with the the three dimensional CT data. These approaches have problems: They require exposure of the bone, subject the patient and surgeons to ionizing radiation, or do both. Ultrasound can also be used to register a preoperative CT scan to the patient. The ultrasound probe is tracked as it passes over the patient and the ultrasound images are aligned to the CT data. This method eliminates the problems of bone exposure and ionizing radiation, but is computationally more difficult because the ultrasound images contain incomplete and unclear bone surfaces. In this work, we compare three methods to register a set of ultrasound images to a CT scan: Iterated Closest Point, Mutual Information and a novel method Points-to-Image. The average Target Registration Error and speed of each method is presented along with a brief summary of their strengths and weaknesses. / Thesis (Master, Computing) -- Queen's University, 2009-01-22 04:21:22.569

No description available.

A integração de diferentes modalidades de imagens médicas possibilita uma análise mais detalhada de seu conteúdo, visando-se um diagnóstico mais preciso da patologia presente. Este processo, conhecido como corregistro, busca o alinhamento das imagens através da transformação rígida (ou não rígida) das mesmas, por algoritmos matemáticos de distorção, translação, rotação e ajuste de escala. A amplitude de cada transformação é determinada por uma medida de similaridade das imagens. Quanto menor a similaridade, maior será a transformação aplicada. Neste sentido, a métrica de similaridade é uma peça chave do processo de corregistro. No presente trabalho, inicialmente são propostas novas definições para o cálculo dos erros de alinhamento nas transformações de translação, rotação e escala, com o objetivo de se avaliar o desempenho do corregistro. Em seguida, cinco experimentos são realizados. No primeiro, a Informação Mútua Generalizada é avaliada como medida de similaridade para corregistro em imagens fractais e cerebrais. Neste caso, os resultados sugerem a viabilidade do emprego desta métrica, pois em geral conduz a erros de alinhamento muito pequenos, mas sem vantagens aparentes em relação à formulação de Shannon. No segundo experimento, um estudo comparativo entre a Informação Mútua e as métricas clássicas (Coeficiente de Correlação, Média dos Quadrados, Diferença de Gradiente e Cardinalidade) é então realizado. Para as imagens binárias analisadas, as métricas com menores valores de erro de alinhamento para os corregistros de translação e rotação foram a Informação Mútua e a Diferença de Gradiente. Para o corregistro de escala, todas as métricas conduziram a erros de alinhamento próximos de zero. No terceiro experimento, o processo de alinhamento é investigado em termos do número de iterações do algoritmo de corregistro. Considerando-se ambas as variáveis erro de alinhamento e número de iterações, conclui-se que o uso da Informação Mútua Generalizada com q = 1.0 é adequado ao corregistro. No quarto experimento, a influência da dimensão fractal no corregistro de imagens fractais binárias foi estudada. Para algumas métricas, a tendência geral observada é a de uma diminuição do erro de alinhamento em resposta ao aumento da dimensão fractal. Finalmente, no quinto experimento, constatou-se a existência de correlação linear entre os erros de alinhamento de imagens em tons de cinza do córtex cerebral e de fractais do conjunto Julia. / The integration of different modalities of medical images provides a detailed analysis of its contents, aiming at a more accurate diagnosis of the pathology. This process, known as coregistration, seeks to align the images through rigid (or non-rigid) transformations, by mathematical algorithms of distortion, translation, rotation and scaling. The amplitude of each transformation is determined by a similarity measure of the images. The lower the similarity, the greater the transformation applied. In this sense, the similarity metric is the key for the coregistration process. In this work, new definitions are proposed for the calculation of alignment errors in the transformations of translation, rotation and scale, with the objective of evaluating the performance of coregistration. Then, five experiments are performed. In the first one, the Generalized Mutual Information is evaluated as a similarity measure for coregistration of brain images and fractals. In this case, the results suggest the feasibility of using this measure, since it leads to very small alignment errors, although no advantages in relation to Shannon formulation are evident. In the second experiment, a comparative study between Mutual Information and the classical metrics (Correlation Coefficient, Mean Squares, Gradient Difference and Cardinality) is performed. For the binary images analyzed, the metrics with lower alignment errors for translation and rotation are the Mutual Information and Gradient Difference. For scaling transformation, all the metrics lead to alignment errors close to zero. In the third experiment, the alignment process is investigated in terms of number of iterations of the coregistration algorithm. Considering both variables alignment error and number of iterations, it is concluded that the use of Generalized Mutual Information with q =1 is appropriate for coregistration. In the fourth experiment, it is studied the influence of fractal dimension in coregistration of binary fractal images. For some metrics, as a general trend, one observes the decay of the alignment error in response to the increase of the fractal dimension. Finally, in the fifth experiment, the results indicate the existence of a linear correlation between the alignment errors of grayscale images of the cerebral cortex and Julia set fractals.

Recent methods to infer genetic networks are based on identifying gene interactions by similarities in expression profiles. These methods are founded on the assumption that interacting genes share higher similarities in their expression profiles than non-interacting genes. In this dissertation this assumption is validated when using mutual information as a similarity measure. Three algorithms that calculate mutual information between expression data are developed: 1) a basic approach implemented with the histogram technique; 2) an extension of the basic approach that takes into consideration time delay between expression profiles; 3) an extension of the basic approach that takes into consideration that genes are regulated in a complex manner by multiple genes. In our experiments we compare the mutual information distributions for profiles of interacting and non-interacting genes. The results show that interacting genes do not share higher mutual information in their expression profiles than non-interacting genes, thus contradicting the basic assumption that similarity measures need to fulfil. This indicates that mutual information is not appropriate as similarity measure, which contradicts earlier proposals.