1,958 research outputs found

    Generalizable automated pixel-level structural segmentation of medical and biological data

    Get PDF
    Over the years, the rapid expansion in imaging techniques and equipments has driven the demand for more automation in handling large medical and biological data sets. A wealth of approaches have been suggested as optimal solutions for their respective imaging types. These solutions span various image resolutions, modalities and contrast (staining) mechanisms. Few approaches generalise well across multiple image types, contrasts or resolution. This thesis proposes an automated pixel-level framework that addresses 2D, 2D+t and 3D structural segmentation in a more generalizable manner, yet has enough adaptability to address a number of specific image modalities, spanning retinal funduscopy, sequential fluorescein angiography and two-photon microscopy. The pixel-level segmentation scheme involves: i ) constructing a phase-invariant orientation field of the local spatial neighbourhood; ii ) combining local feature maps with intensity-based measures in a structural patch context; iii ) using a complex supervised learning process to interpret the combination of all the elements in the patch in order to reach a classification decision. This has the advantage of transferability from retinal blood vessels in 2D to neural structures in 3D. To process the temporal components in non-standard 2D+t retinal angiography sequences, we first introduce a co-registration procedure: at the pairwise level, we combine projective RANSAC with a quadratic homography transformation to map the coordinate systems between any two frames. At the joint level, we construct a hierarchical approach in order for each individual frame to be registered to the global reference intra- and inter- sequence(s). We then take a non-training approach that searches in both the spatial neighbourhood of each pixel and the filter output across varying scales to locate and link microvascular centrelines to (sub-) pixel accuracy. In essence, this \link while extract" piece-wise segmentation approach combines the local phase-invariant orientation field information with additional local phase estimates to obtain a soft classification of the centreline (sub-) pixel locations. Unlike retinal segmentation problems where vasculature is the main focus, 3D neural segmentation requires additional exibility, allowing a variety of structures of anatomical importance yet with different geometric properties to be differentiated both from the background and against other structures. Notably, cellular structures, such as Purkinje cells, neural dendrites and interneurons, all display certain elongation along their medial axes, yet each class has a characteristic shape captured by an orientation field that distinguishes it from other structures. To take this into consideration, we introduce a 5D orientation mapping to capture these orientation properties. This mapping is incorporated into the local feature map description prior to a learning machine. Extensive performance evaluations and validation of each of the techniques presented in this thesis is carried out. For retinal fundus images, we compute Receiver Operating Characteristic (ROC) curves on existing public databases (DRIVE & STARE) to assess and compare our algorithms with other benchmark methods. For 2D+t retinal angiography sequences, we compute the error metrics ("Centreline Error") of our scheme with other benchmark methods. For microscopic cortical data stacks, we present segmentation results on both surrogate data with known ground-truth and experimental rat cerebellar cortex two-photon microscopic tissue stacks.Open Acces

    Automatic solar feature detection using image processing and pattern recognition techniques

    Get PDF
    The objective of the research in this dissertation is to develop a software system to automatically detect and characterize solar flares, filaments and Corona Mass Ejections (CMEs), the core of so-called solar activity. These tools will assist us to predict space weather caused by violent solar activity. Image processing and pattern recognition techniques are applied to this system. For automatic flare detection, the advanced pattern recognition techniques such as Multi-Layer Perceptron (MLP), Radial Basis Function (RBF), and Support Vector Machine (SVM) are used. By tracking the entire process of flares, the motion properties of two-ribbon flares are derived automatically. In the applications of the solar filament detection, the Stabilized Inverse Diffusion Equation (SIDE) is used to enhance and sharpen filaments; a new method for automatic threshold selection is proposed to extract filaments from background; an SVM classifier with nine input features is used to differentiate between sunspots and filaments. Once a filament is identified, morphological thinning, pruning, and adaptive edge linking methods are applied to determine filament properties. Furthermore, a filament matching method is proposed to detect filament disappearance. The automatic detection and characterization of flares and filaments have been successfully applied on Hα full-disk images that are continuously obtained at Big Bear Solar Observatory (BBSO). For automatically detecting and classifying CMEs, the image enhancement, segmentation, and pattern recognition techniques are applied to Large Angle Spectrometric Coronagraph (LASCO) C2 and C3 images. The processed LASCO and BBSO images are saved to file archive, and the physical properties of detected solar features such as intensity and speed are recorded in our database. Researchers are able to access the solar feature database and analyze the solar data efficiently and effectively. The detection and characterization system greatly improves the ability to monitor the evolution of solar events and has potential to be used to predict the space weather

    Fluorescence microscopy tensor imaging representations for large-scale dataset analysis

    Get PDF
    Understanding complex biological systems requires the system-wide characterization of cellular and molecular features. Recent advances in optical imaging technologies and chemical tissue clearing have facilitated the acquisition of whole-organ imaging datasets, but automated tools for their quantitative analysis and visualization are still lacking. We have here developed a visualization technique capable of providing whole-organ tensor imaging representations of local regional descriptors based on fluorescence data acquisition. This method enables rapid, multiscale, analysis and virtualization of large-volume, high-resolution complex biological data while generating 3D tractographic representations. Using the murine heart as a model, our method allowed us to analyze and interrogate the cardiac microvasculature and the tissue resident macrophage distribution and better infer and delineate the underlying structural network in unprecedented detail

    From Molecules to the Masses : Visual Exploration, Analysis, and Communication of Human Physiology

    Get PDF
    Det overordnede mÄlet med denne avhandlingen er tverrfaglig anvendelse av medisinske illustrasjons- og visualiseringsteknikker for Ä utforske, analysere og formidle aspekter ved fysiologi til publikum med ulik faglig nivÄ og bakgrunn. Fysiologi beskriver de biologiske prosessene som skjer i levende vesener over tid. Vitenskapen om fysiologi er kompleks, men samtidig kritisk for vÄr forstÄelse av hvordan levende organismer fungerer. Fysiologi dekker en stor bredde romlig-temporale skalaer og fordrer behovet for Ä kombinere og bygge bro mellom basalfagene (biologi, fysikk og kjemi) og medisin. De senere Ärene har det vÊrt en eksplosjon av nye, avanserte eksperimentelle metoder for Ä detektere og karakterisere fysiologiske data. Volumet og kompleksiteten til fysiologiske data krever effektive strategier for visualisering for Ä komplementere dagens standard analyser. Hvilke tilnÊrminger som benyttes i visualiseringen mÄ nÞye balanseres og tilpasses formÄlet med bruken av dataene, enten dette er for Ä utforske dataene, analysere disse eller kommunisere og presentere dem. Arbeidet i denne avhandlingen bidrar med ny kunnskap innen teori, empiri, anvendelse og reproduserbarhet av visualiseringsmetoder innen fysiologi. FÞrst i avhandlingen er en rapport som oppsummerer og utforsker dagens kunnskap om muligheter og utfordringer for visualisering innen fysiologi. Motivasjonen for arbeidet er behovet forskere innen visualiseringsfeltet, og forskere i ulike anvendelsesomrÄder, har for en sammensatt oversikt over flerskala visualiseringsoppgaver og teknikker. Ved Ä bruke sÞk over et stort spekter av metodiske tilnÊrminger, er dette den fÞrste rapporten i sitt slag som kartlegger visualiseringsmulighetene innen fysiologi. I rapporten er faglitteraturen oppsummert slik at det skal vÊre enkelt Ä gjÞre oppslag innen ulike tema i rom-og-tid-skalaen, samtidig som litteraturen er delt inn i de tre hÞynivÄ visualiseringsoppgavene data utforsking, analyse og kommunikasjon. Dette danner et enkelt grunnlag for Ä navigere i litteraturen i feltet og slik danner rapporten et godt grunnlag for diskusjon og forskningsmuligheter innen feltet visualisering og fysiologi. Basert pÄ arbeidet med rapporten var det sÊrlig to omrÄder som det er Þnskelig for oss Ä fortsette Ä utforske: (1) utforskende analyse av mangefasetterte fysiologidata for ekspertbrukere, og (2) kommunikasjon av data til bÄde eksperter og ikke-eksperter. Arbeidet vÄrt av mangefasetterte fysiologidata er oppsummert i to studier i avhandlingen. Hver studie omhandler prosesser som foregÄr pÄ forskjellige romlig-temporale skalaer og inneholder konkrete eksempler pÄ anvendelse av metodene vurdert av eksperter i feltet. I den fÞrste av de to studiene undersÞkes konsentrasjonen av molekylÊre substanser (metabolitter) ut fra data innsamlet med magnetisk resonansspektroskopi (MRS), en avansert biokjemisk teknikk som brukes til Ä identifisere metabolske forbindelser i levende vev. Selv om MRS kan ha svÊrt hÞy sensitivitet og spesifisitet i medisinske anvendelser, er analyseresultatene fra denne modaliteten abstrakte og vanskelige Ä forstÄ ogsÄ for medisinskfaglige eksperter i feltet. VÄr designstudie som undersÞkte oppgavene og kravene til ekspertutforskende analyse av disse dataene fÞrte til utviklingen av SpectraMosaic. Dette er en ny applikasjon som gjÞr det mulig for domeneeksperter Ä analysere konsentrasjonen av metabolitter normalisert for en hel kohort, eller etter prÞveregion, individ, opptaksdato, eller status pÄ hjernens aktivitetsnivÄ ved undersÞkelsestidspunktet. I den andre studien foreslÄs en metode for Ä utfÞre utforskende analyser av flerdimensjonale fysiologiske data i motsatt ende av den romlig-temporale skalaen, nemlig pÄ populasjonsnivÄ. En effektiv arbeidsflyt for utforskende dataanalyse mÄ kritisk identifisere interessante mÞnstre og relasjoner, noe som blir stadig vanskeligere nÄr dimensjonaliteten til dataene Þker. Selv om dette delvis kan lÞses med eksisterende reduksjonsteknikker er det alltid en fare for at subtile mÞnstre kan gÄ tapt i reduksjonsprosessen. Isteden presenterer vi i studien DimLift, en iterativ dimensjonsreduksjonsteknikk som muliggjÞr brukeridentifikasjon av interessante mÞnstre og relasjoner som kan ligge subtilt i et datasett gjennom dimensjonale bunter. NÞkkelen til denne metoden er brukerens evne til Ä styre dimensjonalitetsreduksjonen slik at den fÞlger brukerens egne undersÞkelseslinjer. For videre Ä undersÞke kommunikasjon til eksperter og ikke-eksperter, studeres i neste arbeid utformingen av visualiseringer for kommunikasjon til publikum med ulike nivÄer av ekspertnivÄ. Det er naturlig Ä forvente at eksperter innen et emne kan ha ulike preferanser og kriterier for Ä vurdere en visuell kommunikasjon i forhold til et ikke-ekspertpublikum. Dette pÄvirker hvor effektivt et bilde kan benyttes til Ä formidle en gitt scenario. Med utgangspunkt i ulike teknikker innen biomedisinsk illustrasjon og visualisering, gjennomfÞrte vi derfor en utforskende studie av kriteriene som publikum bruker nÄr de evaluerer en biomedisinsk prosessvisualisering mÄlrettet for kommunikasjon. Fra denne studien identifiserte vi muligheter for ytterligere konvergens av biomedisinsk illustrasjon og visualiseringsteknikker for mer mÄlrettet visuell kommunikasjonsdesign. SÊrlig beskrives i stÞrre dybde utviklingen av semantisk konsistente retningslinjer for farging av molekylÊre scener. Hensikten med slike retningslinjer er Ä heve den vitenskapelige kompetansen til ikke-ekspertpublikum innen molekyler visualisering, som vil vÊre spesielt relevant for kommunikasjon til befolkningen i forbindelse med folkehelseopplysning. All kode og empiriske funn utviklet i arbeidet med denne avhandlingen er Äpen kildekode og tilgjengelig for gjenbruk av det vitenskapelige miljÞet og offentligheten. Metodene og funnene presentert i denne avhandlingen danner et grunnlag for tverrfaglig biomedisinsk illustrasjon og visualiseringsforskning, og Äpner flere muligheter for fortsatt arbeid med visualisering av fysiologiske prosesser.The overarching theme of this thesis is the cross-disciplinary application of medical illustration and visualization techniques to address challenges in exploring, analyzing, and communicating aspects of physiology to audiences with differing expertise. Describing the myriad biological processes occurring in living beings over time, the science of physiology is complex and critical to our understanding of how life works. It spans many spatio-temporal scales to combine and bridge the basic sciences (biology, physics, and chemistry) to medicine. Recent years have seen an explosion of new and finer-grained experimental and acquisition methods to characterize these data. The volume and complexity of these data necessitate effective visualizations to complement standard analysis practice. Visualization approaches must carefully consider and be adaptable to the user's main task, be it exploratory, analytical, or communication-oriented. This thesis contributes to the areas of theory, empirical findings, methods, applications, and research replicability in visualizing physiology. Our contributions open with a state-of-the-art report exploring the challenges and opportunities in visualization for physiology. This report is motivated by the need for visualization researchers, as well as researchers in various application domains, to have a centralized, multiscale overview of visualization tasks and techniques. Using a mixed-methods search approach, this is the first report of its kind to broadly survey the space of visualization for physiology. Our approach to organizing the literature in this report enables the lookup of topics of interest according to spatio-temporal scale. It further subdivides works according to any combination of three high-level visualization tasks: exploration, analysis, and communication. This provides an easily-navigable foundation for discussion and future research opportunities for audience- and task-appropriate visualization for physiology. From this report, we identify two key areas for continued research that begin narrowly and subsequently broaden in scope: (1) exploratory analysis of multifaceted physiology data for expert users, and (2) communication for experts and non-experts alike. Our investigation of multifaceted physiology data takes place over two studies. Each targets processes occurring at different spatio-temporal scales and includes a case study with experts to assess the applicability of our proposed method. At the molecular scale, we examine data from magnetic resonance spectroscopy (MRS), an advanced biochemical technique used to identify small molecules (metabolites) in living tissue that are indicative of metabolic pathway activity. Although highly sensitive and specific, the output of this modality is abstract and difficult to interpret. Our design study investigating the tasks and requirements for expert exploratory analysis of these data led to SpectraMosaic, a novel application enabling domain researchers to analyze any permutation of metabolites in ratio form for an entire cohort, or by sample region, individual, acquisition date, or brain activity status at the time of acquisition. A second approach considers the exploratory analysis of multidimensional physiological data at the opposite end of the spatio-temporal scale: population. An effective exploratory data analysis workflow critically must identify interesting patterns and relationships, which becomes increasingly difficult as data dimensionality increases. Although this can be partially addressed with existing dimensionality reduction techniques, the nature of these techniques means that subtle patterns may be lost in the process. In this approach, we describe DimLift, an iterative dimensionality reduction technique enabling user identification of interesting patterns and relationships that may lie subtly within a dataset through dimensional bundles. Key to this method is the user's ability to steer the dimensionality reduction technique to follow their own lines of inquiry. Our third question considers the crafting of visualizations for communication to audiences with different levels of expertise. It is natural to expect that experts in a topic may have different preferences and criteria to evaluate a visual communication relative to a non-expert audience. This impacts the success of an image in communicating a given scenario. Drawing from diverse techniques in biomedical illustration and visualization, we conducted an exploratory study of the criteria that audiences use when evaluating a biomedical process visualization targeted for communication. From this study, we identify opportunities for further convergence of biomedical illustration and visualization techniques for more targeted visual communication design. One opportunity that we discuss in greater depth is the development of semantically-consistent guidelines for the coloring of molecular scenes. The intent of such guidelines is to elevate the scientific literacy of non-expert audiences in the context of molecular visualization, which is particularly relevant to public health communication. All application code and empirical findings are open-sourced and available for reuse by the scientific community and public. The methods and findings presented in this thesis contribute to a foundation of cross-disciplinary biomedical illustration and visualization research, opening several opportunities for continued work in visualization for physiology.Doktorgradsavhandlin

    Filtering of image sequences: on line edge detection and motion reconstruction

    Get PDF
    L'argomento della Tesi riguarda líelaborazione di sequenze di immagini, relative ad una scena in cui uno o pi˘ oggetti (possibilmente deformabili) si muovono e acquisite da un opportuno strumento di misura. A causa del processo di misura, le immagini sono corrotte da un livello di degradazione. Si riporta la formalizzazione matematica dellíinsieme delle immagini considerate, dellíinsieme dei moti ammissibili e della degradazione introdotta dallo strumento di misura. Ogni immagine della sequenza acquisita ha una relazione con tutte le altre, stabilita dalla legge del moto della scena. Líidea proposta in questa Tesi Ë quella di sfruttare questa relazione tra le diverse immagini della sequenza per ricostruire grandezze di interesse che caratterizzano la scena. Nel caso in cui si conosce il moto, líinteresse Ë quello di ricostruire i contorni dellíimmagine iniziale (che poi possono essere propagati attraverso la stessa legge del moto, in modo da ricostruire i contorni della generica immagine appartenente alla sequenza in esame), stimando líampiezza e del salto del livello di grigio e la relativa localizzazione. Nel caso duale si suppone invece di conoscere la disposizione dei contorni nellíimmagine iniziale e di avere un modello stocastico che descriva il moto; líobiettivo Ë quindi stimare i parametri che caratterizzano tale modello. Infine, si presentano i risultati dellíapplicazione delle due metodologie succitate a dati reali ottenuti in ambito biomedicale da uno strumento denominato pupillometro. Tali risultati sono di elevato interesse nellíottica di utilizzare il suddetto strumento a fini diagnostici

    Novel Facial Image Recognition Techniques Employing Principal Component Analysis

    Get PDF
    Recently, pattern recognition/classification has received considerable attention in diverse engineering fields such as biomedical imaging, speaker identification, fingerprint recognition, and face recognition, etc. This study contributes novel techniques for facial image recognition based on the Two dimensional principal component analysis in the transform domain. These algorithms reduce the storage requirements by an order of magnitude and the computational complexity by a factor of 2 while maintaining the excellent recognition accuracy of the recently reported methods. The proposed recognition systems employ different structures, multicriteria and multitransform. In addition, principal component analysis in the transform domain in conjunction with vector quantization is developed which result in further improvement in the recognition accuracy and dimensionality reduction. Experimental results confirm the excellent properties of the proposed algorithms

    Wavelets and sparse methods for image reconstruction and classification in neuroimaging

    Get PDF
    This dissertation contributes to neuroimaging literature in the fields of compressed sensing magnetic resonance imaging (CS-MRI) and image-based detection of Alzheimer’s disease (AD). It consists of three main contributions, based on wavelets and sparse methods. The first contribution is a method for wavelet packet basis optimisation for sparse approximation and compressed sensing reconstruction of magnetic resonance (MR) images of the brain. The proposed method is based on the basis search algorithm developed by Coifman and Wickerhauser, with a cost function designed specifically for compressed sensing. It is tested on MR images available from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). The second contribution consists of evaluating and comparing several sparse classification methods in an application to detection of AD based on positron emission tomography (PET) images of the brain. This comparison includes univariate feature selection, feature clustering and classifiers that automatically select a small subset of features due to their mathematical or algorithmic construction. The evaluation is based on PET images available from ADNI. The third contribution is proposing an extension of wavelet-based scattering networks (originally proposed by Mallat and Bruna) to three-dimensional tomographic images. The proposed extension is evaluated as a feature representation in an application to detection of AD based on MR images available from ADNI. There are several possible extensions of the work presented in this dissertation. The wavelet packet basis search method proposed in the first contribution can be improved to take into account the coherence between the sparse approximation basis and the sensing basis. The evaluation presented in the second contribution can be extended with additional algorithms to make it more comprehensive. The three-dimensional scattering networks that are the core part of the third contribution can be combined with other machine learning methods, such as manifold learning or deep convolutional neural networks. As a whole, the methods proposed in this dissertation contribute to the work towards efficient screening for Alzheimer’s disease, by making MRI scans of the brain faster and helping to automate image analysis for AD detection. The first contribution is a method for wavelet packet basis optimisation for sparse approximation and compressed sensing reconstruction of magnetic resonance (MR) images of the brain. The proposed method is based on the basis search algorithm developed by Coifman and Wickerhauser, with a cost function designed specifically for compressed sensing. It is tested on MR images available from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). The second contribution consists of evaluating and comparing several sparse classification methods in an application to detection of AD based on positron emission tomography (PET) images of the brain. This comparison includes univariate feature selection, feature clustering and classifiers that automatically select a small subset of features due to their mathematical or algorithmic construction. The evaluation is based on PET images available from ADNI. The third contribution is proposing an extension of wavelet-based scattering networks (originally proposed by Mallat and Bruna) to three-dimensional tomographic images. The proposed extension is evaluated as a feature representation in an application to detection of AD based on MR images available from ADNI. There are several possible extensions of the work presented in this dissertation. The wavelet packet basis search method proposed in the first contribution can be improved to take into account the coherence between the sparse approximation basis and the sensing basis. The evaluation presented in the second contribution can be extended with additional algorithms to make it more comprehensive. The three-dimensional scattering networks that are the core part of the third contribution can be combined with other machine learning methods, such as manifold learning or deep convolutional neural networks. This dissertation contributes to neuroimaging literature in the fields of compressed sensing magnetic resonance imaging (CS-MRI) and image-based detection of Alzheimer’s disease (AD). It consists of three main contributions, based on wavelets and sparse methods. The first contribution is a method for wavelet packet basis optimisation for sparse approximation and compressed sensing reconstruction of magnetic resonance (MR) images of the brain. The proposed method is based on the basis search algorithm developed by Coifman and Wickerhauser, with a cost function designed specifically for compressed sensing. It is tested on MR images available from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). The second contribution consists of evaluating and comparing several sparse classification methods in an application to detection of AD based on positron emission tomography (PET) images of the brain. This comparison includes univariate feature selection, feature clustering and classifiers that automatically select a small subset of features due to their mathematical or algorithmic construction. The evaluation is based on PET images available from ADNI. The third contribution is proposing an extension of wavelet-based scattering networks (originally proposed by Mallat and Bruna) to three-dimensional tomographic images. The proposed extension is evaluated as a feature representation in an application to detection of AD based on MR images available from ADNI. There are several possible extensions of the work presented in this dissertation. The wavelet packet basis search method proposed in the first contribution can be improved to take into account the coherence between the sparse approximation basis and the sensing basis. The evaluation presented in the second contribution can be extended with additional algorithms to make it more comprehensive. The three-dimensional scattering networks that are the core part of the third contribution can be combined with other machine learning methods, such as manifold learning or deep convolutional neural networks. As a whole, the methods proposed in this dissertation contribute to the work towards efficient screening for Alzheimer’s disease, by making MRI scans of the brain faster and helping to automate image analysis for AD detection.Open Acces
    • 

    corecore