25 research outputs found

    A Method of Protein Model Classification and Retrieval Using Bag-of-Visual-Features

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    In this paper we propose a novel visual method for protein model classification and retrieval. Different from the conventional methods, the key idea of the proposed method is to extract image features of proteins and measure the visual similarity between proteins. Firstly, the multiview images are captured by vertices and planes of a given octahedron surrounding the protein. Secondly, the local features are extracted from each image of the different views by the SURF algorithm and are vector quantized into visual words using a visual codebook. Finally, KLD is employed to calculate the similarity distance between two feature vectors. Experimental results show that the proposed method has encouraging performances for protein retrieval and categorization as shown in the comparison with other methods

    Irish Machine Vision and Image Processing Conference Proceedings 2017

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    Image Processing and Analysis for Preclinical and Clinical Applications

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    Radiomics is one of the most successful branches of research in the field of image processing and analysis, as it provides valuable quantitative information for the personalized medicine. It has the potential to discover features of the disease that cannot be appreciated with the naked eye in both preclinical and clinical studies. In general, all quantitative approaches based on biomedical images, such as positron emission tomography (PET), computed tomography (CT) and magnetic resonance imaging (MRI), have a positive clinical impact in the detection of biological processes and diseases as well as in predicting response to treatment. This Special Issue, “Image Processing and Analysis for Preclinical and Clinical Applications”, addresses some gaps in this field to improve the quality of research in the clinical and preclinical environment. It consists of fourteen peer-reviewed papers covering a range of topics and applications related to biomedical image processing and analysis

    Nephroblastoma in MRI Data

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    The main objective of this work is the mathematical analysis of nephroblastoma in MRI sequences. At the beginning we provide two different datasets for segmentation and classification. Based on the first dataset, we analyze the current clinical practice regarding therapy planning on the basis of annotations of a single radiologist. We can show with our benchmark that this approach is not optimal and that there may be significant differences between human annotators and even radiologists. In addition, we demonstrate that the approximation of the tumor shape currently used is too coarse granular and thus prone to errors. We address this problem and develop a method for interactive segmentation that allows an intuitive and accurate annotation of the tumor. While the first part of this thesis is mainly concerned with the segmentation of Wilms’ tumors, the second part deals with the reliability of diagnosis and the planning of the course of therapy. The second data set we compiled allows us to develop a method that dramatically improves the differential diagnosis between nephroblastoma and its precursor lesion nephroblastomatosis. Finally, we can show that even the standard MRI modality for Wilms’ tumors is sufficient to estimate the developmental tendencies of nephroblastoma under chemotherapy

    Entropy in Image Analysis III

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    Image analysis can be applied to rich and assorted scenarios; therefore, the aim of this recent research field is not only to mimic the human vision system. Image analysis is the main methods that computers are using today, and there is body of knowledge that they will be able to manage in a totally unsupervised manner in future, thanks to their artificial intelligence. The articles published in the book clearly show such a future

    Recalage déformable à base de graphes : mise en correspondance coupe-vers-volume et méthodes contextuelles

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    Image registration methods, which aim at aligning two or more images into one coordinate system, are among the oldest and most widely used algorithms in computer vision. Registration methods serve to establish correspondence relationships among images (captured at different times, from different sensors or from different viewpoints) which are not obvious for the human eye. A particular type of registration algorithm, known as graph-based deformable registration methods, has become popular during the last decade given its robustness, scalability, efficiency and theoretical simplicity. The range of problems to which it can be adapted is particularly broad. In this thesis, we propose several extensions to the graph-based deformable registration theory, by exploring new application scenarios and developing novel methodological contributions.Our first contribution is an extension of the graph-based deformable registration framework, dealing with the challenging slice-to-volume registration problem. Slice-to-volume registration aims at registering a 2D image within a 3D volume, i.e. we seek a mapping function which optimally maps a tomographic slice to the 3D coordinate space of a given volume. We introduce a scalable, modular and flexible formulation accommodating low-rank and high order terms, which simultaneously selects the plane and estimates the in-plane deformation through a single shot optimization approach. The proposed framework is instantiated into different variants based on different graph topology, label space definition and energy construction. Simulated and real-data in the context of ultrasound and magnetic resonance registration (where both framework instantiations as well as different optimization strategies are considered) demonstrate the potentials of our method.The other two contributions included in this thesis are related to how semantic information can be encompassed within the registration process (independently of the dimensionality of the images). Currently, most of the methods rely on a single metric function explaining the similarity between the source and target images. We argue that incorporating semantic information to guide the registration process will further improve the accuracy of the results, particularly in the presence of semantic labels making the registration a domain specific problem.We consider a first scenario where we are given a classifier inferring probability maps for different anatomical structures in the input images. Our method seeks to simultaneously register and segment a set of input images, incorporating this information within the energy formulation. The main idea is to use these estimated maps of semantic labels (provided by an arbitrary classifier) as a surrogate for unlabeled data, and combine them with population deformable registration to improve both alignment and segmentation.Our last contribution also aims at incorporating semantic information to the registration process, but in a different scenario. In this case, instead of supposing that we have pre-trained arbitrary classifiers at our disposal, we are given a set of accurate ground truth annotations for a variety of anatomical structures. We present a methodological contribution that aims at learning context specific matching criteria as an aggregation of standard similarity measures from the aforementioned annotated data, using an adapted version of the latent structured support vector machine (LSSVM) framework.Les mĂ©thodes de recalage d’images, qui ont pour but l’alignement de deux ou plusieurs images dans un mĂȘme systĂšme de coordonnĂ©es, sont parmi les algorithmes les plus anciens et les plus utilisĂ©s en vision par ordinateur. Les mĂ©thodes de recalage servent Ă  Ă©tablir des correspondances entre des images (prises Ă  des moments diffĂ©rents, par diffĂ©rents senseurs ou avec diffĂ©rentes perspectives), lesquelles ne sont pas Ă©videntes pour l’Ɠil humain. Un type particulier d’algorithme de recalage, connu comme « les mĂ©thodes de recalage dĂ©formables Ă  l’aide de modĂšles graphiques » est devenu de plus en plus populaire ces derniĂšres annĂ©es, grĂące Ă  sa robustesse, sa scalabilitĂ©, son efficacitĂ© et sa simplicitĂ© thĂ©orique. La gamme des problĂšmes auxquels ce type d’algorithme peut ĂȘtre adaptĂ© est particuliĂšrement vaste. Dans ce travail de thĂšse, nous proposons plusieurs extensions Ă  la thĂ©orie de recalage dĂ©formable Ă  l’aide de modĂšles graphiques, en explorant de nouvelles applications et en dĂ©veloppant des contributions mĂ©thodologiques originales.Notre premiĂšre contribution est une extension du cadre du recalage Ă  l’aide de graphes, en abordant le problĂšme trĂšs complexe du recalage d’une tranche avec un volume. Le recalage d’une tranche avec un volume est le recalage 2D dans un volume 3D, comme par exemple le mapping d’une tranche tomographique dans un systĂšme de coordonnĂ©es 3D d’un volume en particulier. Nos avons proposĂ© une formulation scalable, modulaire et flexible pour accommoder des termes d'ordre Ă©levĂ© et de rang bas, qui peut sĂ©lectionner le plan et estimer la dĂ©formation dans le plan de maniĂšre simultanĂ©e par une seule approche d'optimisation. Le cadre proposĂ© est instanciĂ© en diffĂ©rentes variantes, basĂ©s sur diffĂ©rentes topologies du graph, dĂ©finitions de l'espace des Ă©tiquettes et constructions de l'Ă©nergie. Le potentiel de notre mĂ©thode a Ă©tĂ© dĂ©montrĂ© sur des donnĂ©es rĂ©elles ainsi que des donnĂ©es simulĂ©es dans le cadre d’une rĂ©sonance magnĂ©tique d’ultrason (oĂč le cadre d’installation et les stratĂ©gies d’optimisation ont Ă©tĂ© considĂ©rĂ©s).Les deux autres contributions inclues dans ce travail de thĂšse, sont liĂ©es au problĂšme de l’intĂ©gration de l’information sĂ©mantique dans la procĂ©dure de recalage (indĂ©pendamment de la dimensionnalitĂ© des images). Actuellement, la plupart des mĂ©thodes comprennent une seule fonction mĂ©trique pour expliquer la similaritĂ© entre l’image source et l’image cible. Nous soutenons que l'intĂ©gration des informations sĂ©mantiques pour guider la procĂ©dure de recalage pourra encore amĂ©liorer la prĂ©cision des rĂ©sultats, en particulier en prĂ©sence d'Ă©tiquettes sĂ©mantiques faisant du recalage un problĂšme spĂ©cifique adaptĂ© Ă  chaque domaine.Nous considĂ©rons un premier scĂ©nario en proposant un classificateur pour infĂ©rer des cartes de probabilitĂ© pour les diffĂ©rentes structures anatomiques dans les images d'entrĂ©e. Notre mĂ©thode vise Ă  recaler et segmenter un ensemble d'images d'entrĂ©e simultanĂ©ment, en intĂ©grant cette information dans la formulation de l'Ă©nergie. L'idĂ©e principale est d'utiliser ces cartes estimĂ©es des Ă©tiquettes sĂ©mantiques (fournie par un classificateur arbitraire) comme un substitut pour les donnĂ©es non-Ă©tiquettĂ©es, et les combiner avec le recalage dĂ©formable pour amĂ©liorer l'alignement ainsi que la segmentation.Notre derniĂšre contribution vise Ă©galement Ă  intĂ©grer l'information sĂ©mantique pour la procĂ©dure de recalage, mais dans un scĂ©nario diffĂ©rent. Dans ce cas, au lieu de supposer que nous avons des classificateurs arbitraires prĂ©-entraĂźnĂ©s Ă  notre disposition, nous considĂ©rons un ensemble d’annotations prĂ©cis (vĂ©ritĂ© terrain) pour une variĂ©tĂ© de structures anatomiques. Nous prĂ©sentons une contribution mĂ©thodologique qui vise Ă  l'apprentissage des critĂšres correspondants au contexte spĂ©cifique comme une agrĂ©gation des mesures de similaritĂ© standard Ă  partir des donnĂ©es annotĂ©es, en utilisant une adaptation de l’algorithme « Latent Structured Support Vector Machine »

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

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    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

    Computational imaging and automated identification for aqueous environments

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    Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy at the Massachusetts Institute of Technology and the Woods Hole Oceanographic Institution June 2011Sampling the vast volumes of the ocean requires tools capable of observing from a distance while retaining detail necessary for biology and ecology, ideal for optical methods. Algorithms that work with existing SeaBED AUV imagery are developed, including habitat classi fication with bag-of-words models and multi-stage boosting for rock sh detection. Methods for extracting images of sh from videos of longline operations are demonstrated. A prototype digital holographic imaging device is designed and tested for quantitative in situ microscale imaging. Theory to support the device is developed, including particle noise and the effects of motion. A Wigner-domain model provides optimal settings and optical limits for spherical and planar holographic references. Algorithms to extract the information from real-world digital holograms are created. Focus metrics are discussed, including a novel focus detector using local Zernike moments. Two methods for estimating lateral positions of objects in holograms without reconstruction are presented by extending a summation kernel to spherical references and using a local frequency signature from a Riesz transform. A new metric for quickly estimating object depths without reconstruction is proposed and tested. An example application, quantifying oil droplet size distributions in an underwater plume, demonstrates the efficacy of the prototype and algorithms.Funding was provided by NOAA Grant #5710002014, NOAA NMFS Grant #NA17RJ1223, NSF Grant #OCE-0925284, and NOAA Grant #NA10OAR417008

    Recalage/Fusion d'images multimodales à l'aide de graphes d'ordres supérieurs

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    The main objective of this thesis is the exploration of higher order Markov Random Fields for image registration, specifically to encode the knowledge of global transformations, like rigid transformations, into the graph structure. Our main framework applies to 2D-2D or 3D-3D registration and use a hierarchical grid-based Markov Random Field model where the hidden variables are the displacements vectors of the control points of the grid.We first present the construction of a graph that allows to perform linear registration, which means here that we can perform affine registration, rigid registration, or similarity registration with the same graph while changing only one potential. Our framework is thus modular regarding the sought transformation and the metric used. Inference is performed with Dual Decomposition, which allows to handle the higher order hyperedges and which ensures the global optimum of the function is reached if we have an agreement among the slaves. A similar structure is also used to perform 2D-3D registration.Second, we fuse our former graph with another structure able to perform deformable registration. The resulting graph is more complex and another optimisation algorithm, called Alternating Direction Method of Multipliers is needed to obtain a better solution within reasonable time. It is an improvement of Dual Decomposition which speeds up the convergence. This framework is able to solve simultaneously both linear and deformable registration which allows to remove a potential bias created by the standard approach of consecutive registrations.L’objectif principal de cette thĂšse est l’exploration du recalage d’images Ă  l’aide de champs alĂ©atoires de Markov d’ordres supĂ©rieurs, et plus spĂ©cifiquement d’intĂ©grer la connaissance de transformations globales comme une transformation rigide, dans la structure du graphe. Notre cadre principal s’applique au recalage 2D-2D ou 3D-3D et utilise une approche hiĂ©rarchique d’un modĂšle de champ de Markov dont le graphe est une grille rĂ©guliĂšre. Les variables cachĂ©es sont les vecteurs de dĂ©placements des points de contrĂŽle de la grille.Tout d’abord nous expliciterons la construction du graphe qui permet de recaler des images en cherchant entre elles une transformation affine, rigide, ou une similaritĂ©, tout en ne changeant qu’un potentiel sur l’ensemble du graphe, ce qui assure une flexibilitĂ© lors du recalage. Le choix de la mĂ©trique est Ă©galement laissĂ©e Ă  l’utilisateur et ne modifie pas le fonctionnement de notre algorithme. Nous utilisons l’algorithme d’optimisation de dĂ©composition duale qui permet de gĂ©rer les hyper-arĂȘtes du graphe et qui garantit l’obtention du minimum exact de la fonction pourvu que l’on ait un accord entre les esclaves. Un graphe similaire est utilisĂ© pour rĂ©aliser du recalage 2D-3D.Ensuite, nous fusionnons le graphe prĂ©cĂ©dent avec un autre graphe construit pour rĂ©aliser le recalage dĂ©formable. Le graphe rĂ©sultant de cette fusion est plus complexe et, afin d’obtenir un rĂ©sultat en un temps raisonnable, nous utilisons une mĂ©thode d’optimisation appelĂ©e ADMM (Alternating Direction Method of Multipliers) qui a pour but d’accĂ©lĂ©rer la convergence de la dĂ©composition duale. Nous pouvons alors rĂ©soudre simultanĂ©ment recalage affine et dĂ©formable, ce qui nous dĂ©barrasse du biais potentiel issu de l’approche classique qui consiste Ă  recaler affinement puis de maniĂšre dĂ©formable
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