Bayesian deconvolution of scanning electron microscopy images using point-spread function estimation and non-local regularization

Microscopy is one of the most essential imaging techniques in life sciences. High-quality images are required in order to solve (potentially life-saving) biomedical research problems. Many microscopy techniques do not achieve sufficient resolution for these purposes, being limited by physical diffraction and hardware deficiencies. Electron microscopy addresses optical diffraction by measuring emitted or transmitted electrons instead of photons, yielding nanometer resolution. Despite pushing back the diffraction limit, blur should still be taken into account because of practical hardware imperfections and remaining electron diffraction. Deconvolution algorithms can remove some of the blur in post-processing but they depend on knowledge of the point-spread function (PSF) and should accurately regularize noise. Any errors in the estimated PSF or noise model will reduce their effectiveness. This paper proposes a new procedure to estimate the lateral component of the point spread function of a 3D scanning electron microscope more accurately. We also propose a Bayesian maximum a posteriori deconvolution algorithm with a non-local image prior which employs this PSF estimate and previously developed noise statistics. We demonstrate visual quality improvements and show that applying our method improves the quality of subsequent segmentation steps

An interactive ImageJ plugin for semi-automated image denoising in electron microscopy

The recent advent of 3D in electron microscopy (EM) has allowed for detection of nanometer resolution structures. This has caused an explosion in dataset size, necessitating the development of automated workflows. Moreover, large 3D EM datasets typically require hours to days to be acquired and accelerated imaging typically results in noisy data. Advanced denoising techniques can alleviate this, but tend to be less accessible to the community due to low-level programming environments, complex parameter tuning or a computational bottleneck. We present DenoisEM: an interactive and GPU accelerated denoising plugin for ImageJ that ensures fast parameter tuning and processing through parallel computing. Experimental results show that DenoisEM is one order of magnitude faster than related software and can accelerate data acquisition by a factor of 4 without significantly affecting data quality. Lastly, we show that image denoising benefits visualization and (semi-)automated segmentation and analysis of ultrastructure in various volume EM datasets

Bayesian image restoration and bacteria detection in optical endomicroscopy

Optical microscopy systems can be used to obtain high-resolution microscopic images of tissue cultures and ex vivo tissue samples. This imaging technique can be translated for in vivo, in situ applications by using optical fibres and miniature optics. Fibred optical endomicroscopy (OEM) can enable optical biopsy in organs inaccessible by any other imaging systems, and hence can provide rapid and accurate diagnosis in a short time. The raw data the system produce is difficult to interpret as it is modulated by a fibre bundle pattern, producing what is called the “honeycomb effect”. Moreover, the data is further degraded due to the fibre core cross coupling problem. On the other hand, there is an unmet clinical need for automatic tools that can help the clinicians to detect fluorescently labelled bacteria in distal lung images. The aim of this thesis is to develop advanced image processing algorithms that can address the above mentioned problems. First, we provide a statistical model for the fibre core cross coupling problem and the sparse sampling by imaging fibre bundles (honeycomb artefact), which are formulated here as a restoration problem for the first time in the literature. We then introduce a non-linear interpolation method, based on Gaussian processes regression, in order to recover an interpretable scene from the deconvolved data. Second, we develop two bacteria detection algorithms, each of which provides different characteristics. The first approach considers joint formulation to the sparse coding and anomaly detection problems. The anomalies here are considered as candidate bacteria, which are annotated with the help of a trained clinician. Although this approach provides good detection performance and outperforms existing methods in the literature, the user has to carefully tune some crucial model parameters. Hence, we propose a more adaptive approach, for which a Bayesian framework is adopted. This approach not only outperforms the proposed supervised approach and existing methods in the literature but also provides computation time that competes with optimization-based methods

Restoration methods for biomedical images in confocal microscopy

Diese Arbeit stellt neue Loesungen zum Problem Bildrestauration im biomedizinischen Bereich vor. Das Konfokal-Mikroskop ist eine verhaeltnismaessig neue Bildungstechnik, die als Standardwerkzeug in biomedizinischen Studien eingesetzt wird. Diese Technik dient zum Sammeln einer Reihe von 2D Bildern der einzelnen Abschnitte innerhalb eines Probestuecks, um eine 3D Darstellung des Gegenstandes zu erzeugen. Trotz seiner verbesserten Belichtungseigenschaften unterliegen die beobachteten Bilder Stoerungen augrund der begrenzten Groesse der Punktantwort (PSF) und das Poisson-Rauschens. Bildrestaurationstechniken versuchen diese Stoerungen herauszurechnen und das Originalbild zu rekonstruieren. Diese Doktorarbeit beginnt mit der Beschreibung des Konfokal-Mikroskops und den Quellen von Artefakten. Dann werden die vorhandenen Bildwiederherstellungsmethoden vorgestellt und verglichen. Die Arbeit ist in drei Teile gegliedert: Im ersten Teil wird eine neue begrenzte blinde Dekonvolutionsmethode eingefuehrt. Durch eine passende Re-Parametrisierung wird dabei a priori Wissen eingebaut. Fuer die PSF wird ein parametrisches Modell, mit einem begrenzten Satz von Basisunktionen benutzt, um Nicht-Negativitaet, zirkulaaere Symmetrie und Limitierung der Frequenzbandbreite sicher zu stellen. Fuer das Bild stellt die quadratische Re-Parametrisierung die Nicht-Negativitaet sicher. Die Entfaltungsmethode wird anhand von simulierten und realen Konfokal-Mikroskopie Daten ausgewertet. Der Vergleich mit einem nicht-parametrisierten Algorithmus zeigt, dass die vorgeschlagene Methode verbesserte Leistung und schnellere Konvergenz erreicht. Im zweiten Teil der Arbeit wird eine neue Methode eingefuehrt, die versucht die anisotrope tiefabhaengige Unschaerfe zu beheben. Wenn roehrenfoermige Gegenstaende -wie Neuronen- abgebildet werden, sind die aufgenommenen Bilder degradiert und die Extraktion der genauen Morphologie der Neuronen wird erschwert. Es wird eine neue Methode vorgeschlagen, mit der sich die PSF ohne irgendein Vorwissen ueber das Belichtungssystem aus dem augenommenen Bild schaetzen laesst. Diese Methode, die auf der Schaetzung des urspruenglichen Gegenstandes basiert ist fuer Faelle verwendbar, in denen der abgebildete Gegenstand eine bekannte Geometrie hat. Mit der vorgeschlagenen Dekonvolutionsmethode werden geometrische Verzerrungen beseitigt und die wiederhergestellten Bilder sind fuer weitere Analysen besser verwendbar. Im dritten Teil wird eine neue Methode zur adaptiven Regularisierung vorgeschlagen. Diese vorgeschlagene Technik passt ihr Verhalten abhaengig von den lokalen Intensitaetsgradient im Bild an. Die neue Technik wird getestet und mit der ''total variation'' und der Tikhonov Regularisierungtechnik verglichen. Die Experimente zeigen, dass mit dem adaptiven Verahren, die Qualitaet der rekonstruierten Bilder verbessert wird.This thesis introduces new solutions to the problem of image restoration in biomedical fields. The confocal microscope is a relatively new imaging technique that is emerging as a standard tool in biomedical studies. This technique is capable of collecting a series of 2D images of single sections inside a specimen to form a 3D image of the object. Despite of its improved imaging properties, the observed images are blurred due to the finite size of the the point spread function and corrupted by Poisson noise due to the counting nature of image detection. Image restoration techniques aim at reversing the degradation and recovering an estimate of the true image. This thesis starts with the description of the confocal microscope and the sources of degradation. Then, the existing image restoration methods are studied and compared. The work done in this thesis is divided into three parts: In the first part, a new constrained blind deconvolution method is introduced. Re-parameterization is used to strictly enforce prior knowledge. A parametric model based on a set of constrained basis functions is used for the PSF to ensure non-negativity, circular symmetry, and band-limitedness. For the image, quadratic re-parameterization ensures non-negativity. The deconvolution method is evaluated on both simulated and real confocal microscopy data sets. The comparison with non-parameteric algorithms shows that the proposed method exhibits improved performance and faster convergence. In the second part, a new method to correct the anisotropic, depth-variant blur is introduced. When objects of tubular-like structure, like neurons, are imaged, the acquired images are degraded and the extraction of accurate morphology of neurons is hampered. A new method to estimate the PSF from the acquired image, without any prior knowledge about the imaging system, is proposed. This method which is based on the estimation of the original object and is suitable for cases in which, the object being imaged has a known geometry. Using the proposed restoration method, geometric distortions are eliminated and the restored images are more suitable for further analysis. In the third part, a new method for adaptive regularization is proposed. The proposed technique adapts its behavior depending on the local activities in the image, as reflected in the magnitude of the intensity gradient. The new technique is tested and compared to both the total variation and the Tikhonov regularization techniques. Experiments show that, using the adaptive technique, the quality of the restored images is improved

Reconstruction, Classification, and Segmentation for Computational Microscopy

This thesis treats two fundamental problems in computational microscopy: image reconstruction for magnetic resonance force microscopy (MRFM) and image classification for electron backscatter diffraction (EBSD). In MRFM, as in many inverse problems, the true point spread function (PSF) that blurs the image may be only partially known. The image quality may suffer from this possible mismatch when standard image reconstruction techniques are applied. To deal with the mismatch, we develop novel Bayesian sparse reconstruction methods that account for possible errors in the PSF of the microscope and for the inherent sparsity of MRFM images. Two methods are proposed: a stochastic method and a variational method. They both jointly estimate the unknown PSF and unknown image. Our proposed framework for reconstruction has the flexibility to incorporate sparsity inducing priors, thus addressing ill-posedness of this non-convex problem, Markov-Random field priors, and can be extended to other image models. To obtain scalable and tractable solutions, a dimensionality reduction technique is applied to the highly nonlinear PSF space. The experiments clearly demonstrate that the proposed methods have superior performance compared to previous methods. In EBSD we develop novel and robust dictionary-based methods for segmentation and classification of grain and sub-grain structures in polycrystalline materials. Our work is the first in EBSD analysis to use a physics-based forward model, called the dictionary, to use full diffraction patterns, and that efficiently classifies patterns into grains, boundaries, and anomalies. In particular, unlike previous methods, our method incorporates anomaly detection directly into the segmentation process. The proposed approach also permits super-resolution of grain mantle and grain boundary locations. Finally, the proposed dictionary-based segmentation method performs uncertainty quantification, i.e. p-values, for the classified grain interiors and grain boundaries. We demonstrate that the dictionary-based approach is robust to instrument drift and material differences that produce small amounts of dictionary mismatch.PhDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/102296/1/seunpark_1.pd

Blind Image Deconvolution of Electron Microscopy Images

V posledních letech se metody slepé dekonvoluce rozšířily do celé řady technických a vědních oborů zejména, když nejsou již limitovány výpočetně. Techniky zpracování signálu založené na slepé dekonvoluci slibují možnosti zlepšení kvality výsledků dosažených zobrazením pomocí elektronového mikroskopu. Hlavním úkolem této práce je formulování problému slepé dekonvoluce obrazů z elektronového mikroskopu a hledání vhodného řešení s jeho následnou implementací a porovnáním s dostupnou funkcí Matlab Image Processing Toolboxu. Úplným cílem je tedy vytvoření algoritmu korigujícícho vady vzniklé v procesu zobrazení v programovém prostředí Matlabu. Navržený přístup je založen na regularizačních technikách slepé dekonvoluce.Blind deconvolution has spread around multiple technical fields in recent years. Problems with computational demands are no more its limitations. Blind deconvolution signal processing techniques are promising solution for enhancement of electron microscope performance. The aim of this work is the problem formulation and proposition of appropriate solution for blind deconvolution of electron microscope images. The final goal is to develop Matlab algorithm correcting aberrations arising from imperfections of image formation and its comparison with built-in Matlab approach implemented in Image Processing Toolbox. Proposed approach is given by regularization techniques of blind deconvolution.