Optimizing Novel ECG Electrodes

FLEXcon has developed novel electrocardiogram electrodes that use a dry interface that does not dehydrate over time, in contrast to the current industry standard Ag/AgCl hydrogel electrodes which require dehydration barriers in packaging and dry out over a few days. The optimized carbon to pressure sensitive adhesive concentration for the minimum material impedance is 10% carbon to 90% PSA. Optimal activation parameters are 200 V, 100 mA, and a 100 ms discharge time. Signal processing of ECG waveforms conclude that the 10% carbon concentration electrodes, at the largest size given by FLEXcon for testing, are able to substitute the hydrogel Ag/AgCl electrodes in a clinical setting

Computer aided diagnosis in radiology

Ankara : The Department of Electrical and Electronics Engineering and Institute of Engineering and Sciences, Bilkent Univ., 1999.Thesis (Ph.D.) -- Bilkent University, 1999.Includes bibliographical references leaves 117-124.Breast cancer is one of the most deadly diseases for middle-aged women. In this thesis, computer-aided diagnosis tools are developed for the detection of breast cancer on mammograms. These tools include a detection scheme for microcalcification clusters which are an early sign of breast cancer, and a method to detect the boundaries of mass lesions. In the first microcalcification detection method we propose, a subband decomposition structure is employed. Contrary to the previous work, the detection is carried out in the subband domain. The mammogram image is first processed by a subband decomposition filter bank. The resulting subimage is analyzed to detect microcalcification clusters. In regions corresponding to the healthy breast tissue the distribution is almost Gaussian. Since microcalcifications are small, isolated bright spots, they produce outliers in the subimages and the distribution of pixels deviates from Gaussian. The subimages are divided into overlapping square regions. In each square region, skewness and kurtosis values are estimated. As third and fourth order correlation parameters, skewness and kurtosis, are measures of the asymmetry and impulsiveness of the distribution, they can be used to find the locations of microcalcification clusters. If the values of these parameters are higher than experimentally determined thresholds then the region is marked as a potential cancer area. Experimental studies indicate that this method successfully detects regions containing microcalcifications. We also propose another microcalcification detection method which uses two- dimensional (2-D) adaptive filtering and a higher order statistics based Gaussianity test. In this method, statistics of the prediction errors are computed to determine whether the samples are from a Gaussian distribution. The prediction error sequence deviates from Gaussianity around microcalcification locations because prediction of microcalcification pixels is more difficult than prediction of the pixels corresponding to healthy breast tissue. Then, we develop a new Gaussianity test which has higher sensitivity to outliers. The scheme which uses this test gives better detection performance compared to the previously proposed methods. Within the detected regions it is possible to segment individual microcalcifications. An outlier labeling and nonlinear subband decomposition based microcalcification segmentation method is also investigated. Two types of lesions, namely mass and stellate lesions, might be indicators of breast cancer. Finally, we propose a snake algorithm based scheme to detect the boundaries of mass lesions on mammograms. This scheme is compared with a recently developed region growing based boundary detection method. It is observed that the snake algorithm results in a more smooth boundary which is consistent with the morphological structure of mass lesions.Gürcan, Metin NafiPh.D

image analysis and processing with applications in proteomics and medicine

This thesis introduces unsupervised image analysis algorithms for the segmentation of several types of images, with an emphasis on proteomics and medical images. Τhe presented algorithms are tailored upon the principles of deformable models and more specific region-based active contours. Two different objectives are pursued. The first is the core issue of unsupervised parameterization in image segmentation, whereas the second is the formulation of a complete model for the segmentation of proteomics images, which is the first to exploit the appealing attributes of active contours. The first major contribution of this thesis is a novel framework for the automated parameterization of region-based active contours. The presented framework aims to endow segmentation results with objectivity and robustness as well as to set domain users free from the cumbersome and time-consuming process of empirical adjustment. It is applicable on various medical imaging modalities and remains insensitive on alterations in the settings of the acquisition devices. The experimental results demonstrate that the presented framework maintains a segmentation quality which is comparable to the one obtained with empirical parameterization. The second major contribution of this thesis is an unsupervised active contour-based model for the segmentation of proteomics images. The presented model copes with crucial issues in 2D-GE image analysis including streaks, artifacts, faint and overlapping spots. In addition, it provides an alternate to the laborious, error-prone process of manual editing, which is required in state-of-the-art 2D-GE image analysis software packages. The experimental results demonstrate that the presented model outperforms 2D-GE image analysis software packages in terms of detection and segmentation quantity metrics

Image Analysis and Processing With Applications in Proteomics and Medicine

Στην παρούσα διατριβή παρουσιάζονται αυτόματοι αλγόριθμοι ανάλυσης εικόνας για την κατάτμηση διαφόρων τύπων εικόνων, με έμφαση στις εικόνες πρωτεομικής και στις ιατρικές εικόνες. Οι προτεινόμενοι αλγόριθμοι βασίζονται στις αρχές των παραμορφώσιμων μοντέλων. Η διατριβή εστιάζει σε δύο κυρίως στόχους: 1) στην επίλυση του σημαντικού προβλήματος της αυτόματης παραμετροποίησης στην κατάτμηση εικόνας, 2) στην διατύπωση ενός ολοκληρωμένου μοντέλου κατάτμησης εικόνων πρωτεομικής. Η πρώτη συνεισφορά είναι ένα πρωτότυπο πλαίσιο αυτόματης παραμετροποίησης των ενεργών περιγραμμάτων περιοχής. Το πλαίσιο εμπλουτίζει τα αποτελέσματα με αντικειμενικότητα και απελευθερώνει τους τελικούς χρήστες από την επίπονη διαδικασία της εμπειρικής ρύθμισης. Εφαρμόζεται σε διάφορους τύπους ιατρικών εικόνων και παραμένει ανεπηρέαστο στις τροποποιήσεις των ρυθμίσεων των συσκευών λήψης των εικόνων αυτών. Τα πειραματικά αποτελέσματα καταδεικνύουν ότι το προτεινόμενο πλαίσιο διατηρεί υψηλή την ποιότητα κατάτμησης, συγκρίσιμη με εκείνη που επιτυγχάνεται με εμπειρική παραμετροποίηση. Η δεύτερη συνεισφορά είναι ένα αυτόματο μοντέλο βασιζόμενο στα ενεργά περιγράμματα για την κατάτμηση εικόνων πρωτεομικής. Το μοντέλο αντιμετωπίζει σημαντικά προβλήματα συμπεριλαμβανομένων των γραμμών, τεχνουργημάτων, αχνών και επικαλυπτομένων κηλίδων. Ακόμη, παρέχει εναλλακτική λύση στην επιρρεπή σε σφάλματα διαδικασία της χειρωνακτικής επεξεργασίας που απαιτείται στα υπάρχοντα πακέτα λογισμικού. Τα πειραματικά αποτελέσματα καταδεικνύουν ότι το προτεινόμενο μοντέλο υπερτερεί των υπαρχόντων πακέτων λογισμικού σε ποσοτικές μετρικές εντοπισμού και κατάτμησης.This thesis introduces unsupervised image analysis algorithms for the segmentation of several types of images, with an emphasis on proteomics and medical images. Τhe presented algorithms are tailored upon the principles of deformable models. Two objectives are pursued: 1) the core issue of unsupervised parameterization in image segmentation, 2) the formulation of a complete model for the segmentation of proteomics images. The first contribution is a novel framework for automated parameterization of region-based active contours. The presented framework endows segmentation results with objectivity and sets domain users free from the cumbersome process of empirical adjustment. It is applicable on various medical imaging modalities and remains insensitive on alterations in the settings of acquisition devices. The experimental results demonstrate that the presented framework maintains a high segmentation quality, comparable to the one obtained with empirical parameterization. The second contribution is an unsupervised active contour-based model for the segmentation of proteomics images. The presented model copes with crucial issues including streaks, artifacts, faint and overlapping spots. Moreover, it provides an alternate to the error-prone process of manual editing, required in state-of-the-art software packages. The experimental results demonstrate that the proposed model outperforms software packages in terms of detection and segmentation quantity metrics

Automatic improvements of images from 1D gel electrophoresis

V této bakalářské práci jsou vysvětleny základní principy elektroforézy a jejích modalit se zaměřením na 1D gelovou elektroforézu. Popisuje analýzu elektroforeogramu a příčiny jeho zkreslení, a uvádí specifikace aplikací metody v mikrobiologii, genomice a proteomice. Praktická část prezentuje vývoj, optimalizaci a výstupy programu pro automatickou analýzu elektroforeogramů, vytvořeného v prostředí Matlab. Analýza zahrnuje detekci linií a proužků v obrazu a výpočet molekulárních hmotností vzorků. Závěr práce tvoří hodnocení účinnosti detekce a přesnosti výpočtu hmotností.In this bachelor’s thesis are explained the basic principles of electrophoresis and its modalities with focusing on 1D gel electrophoresis. It describes analysis of an electrophoreogram and causes of its possible distortion, and states specifications of applications of the method in microbiology, genomics and proteomics. The practical part presents development, optimization and outputs of a programme for automatic electrophoreogram analysis, which was created in Matlab environment. The analysis contains lane and band detection and computation of samples’ molecular weight. The ending of the thesis is constituted by evaluating efficiency of detection and accuracy of weight computation.

Spatiotemporal localization of proteins in microorganisms via photoactivated localization microscopy

Photoactivated localization microscopy (PALM) is a single molecule fluorescence microscopy technique (SMLM) that relies on the controlled activation and imaging of photo-activatable/convertible fluorescent proteins to determine their position with nanometer scale precision. The analysis of SMLM data is composed of two sequential aspects: the generation of a super-resolution table/image and the subsequent analysis. In recent years, several data analysis packages dedicated to the generation of super-resolved images have been developed. These packages have been extensively characterized and compared in a community-wide effort, therefore allowing researchers to identify optimal solutions for their experiments and providing software developers with a gold standard. On the contrary, the development of data analysis packages dedicated to the study of protein coordinates has been lagging behind, and no comprehensive approach has been developed to date. Here, I present a combination of Fiji and R based scripts for the characterization, filtering and quality assurance of SMLM derived localizations. Furthermore, I demonstrate that specific conventional image analysis techniques can be applied, both quantitatively and qualitatively, to super resolution images. I then apply these analysis tools exemplarily on the characterization of the spatio-temporal localization of a novel DNA repair system in Corynebacterium glutamicum, termed Dip (DNA damage induced protein) C. Finally, I combine the multiple data analysis packages that I developed and/or adapted for the study of specific biological scenarios into a single cohesive pipeline, therefore providing a generalized and comprehensive approach toward the coordinate based analysis of the spatio-temporal localization of proteins in PALM and, in general, in SMLM. Each of the data analysis packages that comprise the pipeline is here presented together with the biological scenario that prompted its development. These include the study of magnetosome formation in Magnetospirillum gryphiswaldense, the study of the chromosome segregation machinery in C. glutamicum and the study of flagellar organization in Trypanosoma brucei.Die photoaktivierte Lokalisationsmikroskopie (PALM) ist eine Einzelmolekül-Fluoreszenzmikroskopie Technik (SMLM), die auf der kontrollierten Aktivierung und Aufnahme von photoaktivierbaren / konvertierbaren fluoreszierenden Proteinen beruht, um ihre Position mit einer Präzision im Nanometerbereich zu bestimmen. Die Analyse von SMLM-Daten besteht aus zwei aufeinander folgenden Aspekten: der Erzeugung einer Tabelle / eines hochauflösenden Bildes und der anschließenden Analyse. In den letzten Jahren wurden mehrere Datenanalysepakete entwickelt, die sich der Berechnung der hochaufgelösten Bilder widmen. Diese Pakete wurden in gemeinschaftsweiten Anstrengungen umfassend charakterisiert und verglichen, sodass Forscher eine optimale Lösung für eigene Experimente wählen können, während Softwareentwicklern einen Goldstandard zur Hand haben. Gegensätzlich wurde jedoch die Entwicklung von Datenanalysepaketen zur spezifischen Untersuchung von Proteinkoordinaten vernachlässigt, so dass in diesem Bereich keine umfassenden Instrumente existieren. In dieser Arbeit präsentiere ich eine Kombination aus Fiji- und R basierten Skripten zur Charakterisierung, Filterung und Qualitätssicherung von SMLM Proteinkoordinaten. Darüber hinaus zeige ich, dass bestimmte konventionelle Bildanalysetechniken sowohl quantitativ als auch qualitativ auf „Superresolution“ Bilder angewandt werden können. Im Folgenden verwende Ich diese Analysewerkzeuge dann beispielhaft zur Charakterisierung der räumlich-zeitlichen Lokalisierung eines neuartigen DNA-Reparatursystems in Corynebacterium glutamicum, welches ich DipC (DNA-Schaden-induziertes Protein) genannt habe. Schließlich kombiniere ich die genannten Datenanalysepakete, die ich für die Untersuchung spezifischer biologischer Szenarien entwickelt und / oder angepasst habe, zu einer einzigen zusammenhängenden Arbeitsroutine. Diese bietet einen allgemeinen und umfassenden Ansatz für die koordinatenbasierte Analyse der räumlich-zeitlichen Lokalisierung von Proteinen aus PALM- und im Allgemeinen aus SMLM-Experimenten. Jedes der Datenanalysepakete, die in beschriebener Routine enthalten sind, wird hier zusammen mit dem biologischen Szenario vorgestellt, das zu ihrer Entwicklung geführt hat. Dazu gehören die Untersuchung der Magnetosomenbildung in Magnetospirillum gryphiswaldense, die Untersuchung der Chromosomensegregationsmaschinerie in C. glutamicum und die Untersuchung der Flagellenorganisation in Trypanosoma brucei