87 research outputs found
Advanced image processing techniques for detection and quantification of drusen
Dissertation presented to obtain the degree of Doctor of Philosophy in Electrical Engineering, speciality on Perceptional Systems, by the Universidade Nova de Lisboa,
Faculty of Sciences and TechnologyDrusen are common features in the ageing macula, caused by accumulation of extracellular materials beneath the retinal surface, visible in retinal fundus images as yellow spots.
In the ophthalmologists’ opinion, the evaluation of the total drusen area, in a sequence of images taken during a treatment, will help to understand the disease progression and effectiveness. However, this evaluation is fastidious and difficult to reproduce when performed manually.
A literature review on automated drusen detection showed that the works already
published were limited to techniques of either adaptive or global thresholds which showed a tendency to produce a significant number of false positives. The purpose for this work was to propose an alternative method to automatically quantify drusen using advanced digital image processing techniques.
This methodology is based on a detection and modelling algorithm to automatically
quantify drusen. It includes an image pre-processing step to correct the uneven illumination by using smoothing splines fitting and to normalize the contrast. To quantify drusen a detection and modelling algorithm is adopted. The detection uses a new gradient based segmentation algorithm that isolates drusen and provides basic drusen characterization to the modelling stage. These are then fitted by Gaussian functions, to produce a model of the
image, which is used to compute the affected areas.
To validate the methodology, two software applications, one for semi-automated
(MD3RI) and other for automated detection of drusen (AD3RI), were implemented. The first
was developed for Ophthalmologists to manually analyse and mark drusen deposits, while the other implemented algorithms for automatic drusen quantification.Four studies to assess the methodology accuracy involving twelve specialists have
taken place. These compared the automated method to the specialists and evaluated its
repeatability. The studies were analysed regarding several indicators, which were based on the
total affected area and on a pixel-to-pixel analysis. Due to the high variability among the
graders involved in the first study, a new evaluation method, the Weighed Matching Analysis,
was developed to improve the pixel-to-pixel analysis by using the statistical significance of
the observations to differentiate positive and negative pixels.
From the results of these studies it was concluded that the methodology proposed is
capable to automatically measure drusen in an accurate and reproducible process. Also, the thesis proposes new image processing algorithms, for image pre-processing, image
segmentation,image modelling and images comparison, which are also applicable to other image processing fields
Entropy in Image Analysis III
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
Biomedical Image Processing and Classification
Biomedical image processing is an interdisciplinary field involving a variety of disciplines, e.g., electronics, computer science, physics, mathematics, physiology, and medicine. Several imaging techniques have been developed, providing many approaches to the study of the human body. Biomedical image processing is finding an increasing number of important applications in, for example, the study of the internal structure or function of an organ and the diagnosis or treatment of a disease. If associated with classification methods, it can support the development of computer-aided diagnosis (CAD) systems, which could help medical doctors in refining their clinical picture
Quantification of tumour heterogenity in MRI
Cancer is the leading cause of death that touches us all, either directly or indirectly.
It is estimated that the number of newly diagnosed cases in the Netherlands will increase
to 123,000 by the year 2020. General Dutch statistics are similar to those in
the UK, i.e. over the last ten years, the age-standardised incidence rate1 has stabilised
at around 355 females and 415 males per 100,000. Figure 1 shows the cancer incidence
per gender. In the UK, the rise in lifetime risk of cancer is more than one in three and depends on many factors, including age, lifestyle and genetic makeup
Mapping three-dimensional geological features from remotely-sensed images and digital elevation models.
Accurate mapping of geological structures is important in numerous applications, ranging from mineral exploration through to hydrogeological modelling. Remotely sensed data can provide synoptic views of study areas enabling mapping of geological units within the area. Structural information may be derived from such data using standard manual photo-geologic interpretation techniques, although these are often inaccurate and incomplete. The aim of this thesis is, therefore, to compile a suite of automated and interactive computer-based analysis routines, designed to help a the user map geological structure. These are examined and integrated in the context of an expert system. The data used in this study include Digital Elevation Model (DEM) and Airborne Thematic Mapper images, both with a spatial resolution of 5m, for a 5 x 5 km area surrounding Llyn Cow lyd, Snowdonia, North Wales. The geology of this area comprises folded and faulted Ordo vician sediments intruded throughout by dolerite sills, providing a stringent test for the automated and semi-automated procedures. The DEM is used to highlight geomorphological features which may represent surface expressions of the sub-surface geology. The DEM is created from digitized contours, for which kriging is found to provide the best interpolation routine, based on a number of quantitative measures. Lambertian shading and the creation of slope and change of slope datasets are shown to provide the most successful enhancement of DEMs, in terms of highlighting a range of key geomorphological features. The digital image data are used to identify rock outcrops as well as lithologically controlled features in the land cover. To this end, a series of standard spectral enhancements of the images is examined. In this respect, the least correlated 3 band composite and a principal component composite are shown to give the best visual discrimination of geological and vegetation cover types. Automatic edge detection (followed by line thinning and extraction) and manual interpretation techniques are used to identify a set of 'geological primitives' (linear or arc features representing lithological boundaries) within these data. Inclusion of the DEM data provides the three-dimensional co-ordinates of these primitives enabling a least-squares fit to be employed to calculate dip and strike values, based, initially, on the assumption of a simple, linearly dipping structural model. A very large number of scene 'primitives' is identified using these procedures, only some of which have geological significance. Knowledge-based rules are therefore used to identify the relevant. For example, rules are developed to identify lake edges, forest boundaries, forest tracks, rock-vegetation boundaries, and areas of geomorphological interest. Confidence in the geological significance of some of the geological primitives is increased where they are found independently in both the DEM and remotely sensed data. The dip and strike values derived in this way are compared to information taken from the published geological map for this area, as well as measurements taken in the field. Many results are shown to correspond closely to those taken from the map and in the field, with an error of < 1°. These data and rules are incorporated into an expert system which, initially, produces a simple model of the geological structure. The system also provides a graphical user interface for manual control and interpretation, where necessary. Although the system currently only allows a relatively simple structural model (linearly dipping with faulting), in the future it will be possible to extend the system to model more complex features, such as anticlines, synclines, thrusts, nappes, and igneous intrusions
New Methods to Improve Large-Scale Microscopy Image Analysis with Prior Knowledge and Uncertainty
Multidimensional imaging techniques provide powerful ways to examine various kinds of scientific questions. The routinely produced data sets in the terabyte-range, however, can hardly be analyzed manually and require an extensive use of automated image analysis. The present work introduces a new concept for the estimation and propagation of uncertainty involved in image analysis operators and new segmentation algorithms that are suitable for terabyte-scale analyses of 3D+t microscopy images
New Methods to Improve Large-Scale Microscopy Image Analysis with Prior Knowledge and Uncertainty
Multidimensional imaging techniques provide powerful ways to examine various
kinds of scientific questions. The routinely produced datasets in the
terabyte-range, however, can hardly be analyzed manually and require an
extensive use of automated image analysis. The present thesis introduces a new
concept for the estimation and propagation of uncertainty involved in image
analysis operators and new segmentation algorithms that are suitable for
terabyte-scale analyses of 3D+t microscopy images.Comment: 218 pages, 58 figures, PhD thesis, Department of Mechanical
Engineering, Karlsruhe Institute of Technology, published online with KITopen
(License: CC BY-SA 3.0, http://dx.doi.org/10.5445/IR/1000057821
Machine learning methods for the characterization and classification of complex data
This thesis work presents novel methods for the analysis and classification of medical images and, more generally, complex data. First, an unsupervised machine learning method is proposed to order anterior chamber OCT (Optical Coherence Tomography) images according to a patient's risk of developing angle-closure glaucoma. In a second study, two outlier finding techniques are proposed to improve the results of above mentioned machine learning algorithm, we also show that they are applicable to a wide variety of data, including fraud detection in credit card transactions. In a third study, the topology of the vascular network of the retina, considering it a complex tree-like network is analyzed and we show that structural differences reveal the presence of glaucoma and diabetic retinopathy. In a fourth study we use a model of a laser with optical injection that presents extreme events in its intensity time-series to evaluate machine learning methods to forecast such extreme events.El presente trabajo de tesis desarrolla nuevos métodos para el análisis y clasificación de imágenes médicas y datos complejos en general. Primero, proponemos un método de aprendizaje automático sin supervisión que ordena imágenes OCT (tomografÃa de coherencia óptica) de la cámara anterior del ojo en función del grado de riesgo del paciente de padecer glaucoma de ángulo cerrado. Luego, desarrollamos dos métodos de detección automática de anomalÃas que utilizamos para mejorar los resultados del algoritmo anterior, pero que su aplicabilidad va mucho más allá, siendo útil, incluso, para la detección automática de fraudes en transacciones de tarjetas de crédito. Mostramos también, cómo al analizar la topologÃa de la red vascular de la retina considerándola una red compleja, podemos detectar la presencia de glaucoma y de retinopatÃa diabética a través de diferencias estructurales. Estudiamos también un modelo de un láser con inyección óptica que presenta eventos extremos en la serie temporal de intensidad para evaluar diferentes métodos de aprendizaje automático para predecir dichos eventos extremos.Aquesta tesi desenvolupa nous mètodes per a l’anà lisi i la classificació d’imatges mèdiques i dades complexes. Hem proposat, primer, un mètode d’aprenentatge automà tic sense supervisió que ordena
imatges OCT (tomografia de coherència òptica) de la cambra anterior de l’ull en funció del grau de risc del pacient de patir glaucoma d’angle tancat. Després, hem desenvolupat dos mètodes de detecció automà tica d’anomalies que hem utilitzat per millorar els resultats de l’algoritme anterior, però que la seva aplicabilitat va molt més enllà , sent útil, fins i tot, per a la detecció automà tica de fraus en transaccions de targetes de crèdit. Mostrem també, com en analitzar la topologia de la xarxa vascular de la retina considerant-la una xarxa complexa, podem detectar la presència de glaucoma i de retinopatia diabètica a través de diferències estructurals. Finalment, hem estudiat un là ser amb injecció òptica, el qual presenta esdeveniments extrems en la sèrie temporal d’intensitat.
Hem avaluat diferents mètodes per tal de predir-los.Postprint (published version
Amélioration de l'image et la segmentation (applications en imagerie médicale)
Avancement dans l'acquisition d'image et le progrès dans les méthodes de traitement d'image ont apporté les mathématiciens et les informaticiens dans les domaines qui sont d'une importance énorme pour les médecins et les biologistes. Le diagnostic précoce de maladies (comme la cécité, le cancer et les problèmes digestifs) ont été des domaines d'intérêt en médecine. Développement des équipements comme microscope bi-photonique à balayage laser et microscope de fluorescence par réflexion totale interne fournit déjà une bonne idée des caractéristiques très intéressantes sur l'objet observé. Cependant, certaines images ne sont pas appropriés pour extraire suffisamment d'informations sur de cette image. Les méthodes de traitement d'image ont été fournit un bon soutien à extraire des informations utiles sur les objets d'intérêt dans ces images biologiques. Rapide méthodes de calcul permettent l'analyse complète, dans un temps très court, d'une série d'images, offrant une assez bonne idée sur les caractéristiques souhaitées. La thèse porte sur l'application de ces méthodes dans trois séries d'images destinées à trois différents types de diagnostic ou d'inférence. Tout d'abord, Images de RP-muté rétine ont été traités pour la détection des cônes, où il n'y avait pas de bâtonnets présents. Le logiciel a été capable de détecter et de compter le nombre de cônes dans chaque image. Deuxièmement, un processus de gastrulation chez la drosophile a été étudié pour observer toute la mitose et les résultats étaient cohérents avec les recherches récentes. Enfin, une autre série d'images ont été traités où la source était une vidéo à partir d'un microscopie photonique à balayage laser. Dans cette vidéo, des objets d'intérêt sont des cellules biologiques. L'idée était de suivre les cellules si elles subissent une mitose. La position de la cellule, la dispersion spatiale et parfois le contour de la membrane cellulaire sont globalement les facteurs limitant la précision dans cette vidéo. Des méthodes appropriées d'amélioration de l'image et de segmentation ont été choisies pour développer une méthode de calcul pour observer cette mitose. L'intervention humaine peut être requise pour éliminer toute inférence fausse.Advancement in Image Acquisition Equipment and progress in Image Processing Methods have brought the mathematicians and computer scientists into areas which are of huge importance for physicians and biologists. Early diagnosis of diseases like blindness, cancer and digestive problems have been areas of interest in medicine. Development of Laser Photon Microscopy and other advanced equipment already provides a good idea of very interesting characteristics of the object being viewed. Still certain images are not suitable to extract sufficient information out of that image. Image Processing methods have been providing good support to provide useful information about the objects of interest in these biological images. Fast computational methods allow complete analysis, in a very short time, of a series of images, providing a reasonably good idea about the desired characteristics. The thesis covers application of these methods in 3 series of images intended for 3 different types of diagnosis or inference. Firstly, Images of RP-mutated retina were treated for detection of rods, where there were no cones present. The software was able to detect and count the number of cones in each frame. Secondly, a gastrulation process in drosophila was studied to observe any mitosis and results were consistent with recent research. Finally, another series of images were treated where biological cells were observed to undergo mitosis. The source was a video from a photon laser microscope. In this video, objects of interest were biological cells. The idea was to track the cells if they undergo mitosis. Cell position, spacing and sometimes contour of the cell membrane are broadly the factors limiting the accuracy in this video. Appropriate method of image enhancement and segmentation were chosen to develop a computational method to observe this mitosis. Cases where human intervention may be required have been proposed to eliminate any false inference.SAVOIE-SCD - Bib.électronique (730659901) / SudocGRENOBLE1/INP-Bib.électronique (384210012) / SudocGRENOBLE2/3-Bib.électronique (384219901) / SudocSudocFranceF
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