Visual Quality Enhancement in Optoacoustic Tomography using Active Contour Segmentation Priors

Segmentation of biomedical images is essential for studying and characterizing anatomical structures, detection and evaluation of pathological tissues. Segmentation has been further shown to enhance the reconstruction performance in many tomographic imaging modalities by accounting for heterogeneities of the excitation field and tissue properties in the imaged region. This is particularly relevant in optoacoustic tomography, where discontinuities in the optical and acoustic tissue properties, if not properly accounted for, may result in deterioration of the imaging performance. Efficient segmentation of optoacoustic images is often hampered by the relatively low intrinsic contrast of large anatomical structures, which is further impaired by the limited angular coverage of some commonly employed tomographic imaging configurations. Herein, we analyze the performance of active contour models for boundary segmentation in cross-sectional optoacoustic tomography. The segmented mask is employed to construct a two compartment model for the acoustic and optical parameters of the imaged tissues, which is subsequently used to improve accuracy of the image reconstruction routines. The performance of the suggested segmentation and modeling approach are showcased in tissue-mimicking phantoms and small animal imaging experiments.Comment: Accepted for publication in IEEE Transactions on Medical Imagin

Development of an Atlas-Based Segmentation of Cranial Nerves Using Shape-Aware Discrete Deformable Models for Neurosurgical Planning and Simulation

Twelve pairs of cranial nerves arise from the brain or brainstem and control our sensory functions such as vision, hearing, smell and taste as well as several motor functions to the head and neck including facial expressions and eye movement. Often, these cranial nerves are difficult to detect in MRI data, and thus represent problems in neurosurgery planning and simulation, due to their thin anatomical structure, in the face of low imaging resolution as well as image artifacts. As a result, they may be at risk in neurosurgical procedures around the skull base, which might have dire consequences such as the loss of eyesight or hearing and facial paralysis. Consequently, it is of great importance to clearly delineate cranial nerves in medical images for avoidance in the planning of neurosurgical procedures and for targeting in the treatment of cranial nerve disorders. In this research, we propose to develop a digital atlas methodology that will be used to segment the cranial nerves from patient image data. The atlas will be created from high-resolution MRI data based on a discrete deformable contour model called 1-Simplex mesh. Each of the cranial nerves will be modeled using its centerline and radius information where the centerline is estimated in a semi-automatic approach by finding a shortest path between two user-defined end points. The cranial nerve atlas is then made more robust by integrating a Statistical Shape Model so that the atlas can identify and segment nerves from images characterized by artifacts or low resolution. To the best of our knowledge, no such digital atlas methodology exists for segmenting nerves cranial nerves from MRI data. Therefore, our proposed system has important benefits to the neurosurgical community

Components of bottom-up gaze allocation in natural images

Recent research [Parkhurst, D., Law, K., & Niebur, E., 2002. Modeling the role of salience in the allocation of overt visual attention. Vision Research 42 (1) (2002) 107–123] showed that a model of bottom-up visual attention can account in part for the spatial locations fixated by humans while free-viewing complex natural and artificial scenes. That study used a definition of salience based on local detectors with coarse global surround inhibition. Here, we use a similar framework to investigate the roles of several types of non-linear interactions known to exist in visual cortex, and of eccentricity-dependent processing. For each of these, we added a component to the salience model, including richer interactions among orientation-tuned units, both at spatial short range (for clutter reduction) and long range (for contour facilitation), and a detailed model of eccentricity-dependent changes in visual processing. Subjects free-viewed naturalistic and artificial images while their eye movements were recorded, and the resulting fixation locations were compared with the models’ predicted salience maps. We found that the proposed interactions indeed play a significant role in the spatiotemporal deployment of attention in natural scenes; about half of the observed inter-subject variance can be explained by these different models. This suggests that attentional guidance does not depend solely on local visual features, but must also include the effects of interactions among features. As models of these interactions become more accurate in predicting behaviorally-relevant salient locations, they become useful to a range of applications in computer vision and human-machine interface design

Implementation of target tracking in Smart Wheelchair Component System

Independent mobility is critical to individuals of any age. While the needs of many individuals with disabilities can be satisfied with power wheelchairs, some members of the disabled community find it difficult or impossible to operate a standard power wheelchair. This population includes, but is not limited to, individuals with low vision, visual field neglect, spasticity, tremors, or cognitive deficits. To meet the needs of this population, our group is involved in developing cost effective modularly designed Smart Wheelchairs. Our objective is to develop an assistive navigation system which will seamlessly integrate into the lifestyle of individual with disabilities and provide safe and independent mobility and navigation without imposing an excessive physical or cognitive load. The Smart Wheelchair Component System (SWCS) can be added to a variety of commercial power wheelchairs with minimal modification to provide navigation assistance. Previous versions of the SWCS used acoustic and infrared rangefinders to identify and avoid obstacles, but these sensors do not lend themselves to many desirable higher-level behaviors. To achieve these higher level behaviors we integrated a Continuously Adapted Mean Shift (CAMSHIFT) target tracking algorithm into the SWCS, along with the Minimal Vector Field Histogram (MVFH) obstacle avoidance algorithm. The target tracking algorithm provides the basis for two distinct operating modes: (1) a "follow-the-leader" mode, and (2) a "move to stationary target" mode.The ability to track a stationary or moving target will make smart wheelchairs more useful as a mobility aid, and is also expected to be useful for wheeled mobility training and evaluation. In addition to wheelchair users, the caregivers, clinicians, and transporters who provide assistance to wheelchair users will also realize beneficial effects of providing safe and independent mobility to wheelchair users which will reduce the level of assistance needed by wheelchair users

Statistical and image processing techniques for remote sensing in agricultural monitoring and mapping

Throughout most of history, increasing agricultural production has been largely driven by expanded land use, and – especially in the 19th and 20th century – by technological innovation in breeding, genetics and agrochemistry as well as intensification through mechanization and industrialization. More recently, information technology, digitalization and automation have started to play a more significant role in achieving higher productivity with lower environmental impact and reduced use of resources. This includes two trends on opposite scales: precision farming applying detailed observations on sub-field level to support local management, and large-scale agricultural monitoring observing regional patterns in plant health and crop productivity to help manage macroeconomic and environmental trends. In both contexts, remote sensing imagery plays a crucial role that is growing due to decreasing costs and increasing accessibility of both data and means of processing and analysis. The large archives of free imagery with global coverage, can be expected to further increase adoption of remote sensing techniques in coming years. This thesis addresses multiple aspects of remote sensing in agriculture by presenting new techniques in three distinct research topics: (1) remote sensing data assimilation in dynamic crop models; (2) agricultural field boundary detection from remote sensing observations; and (3) contour extraction and field polygon creation from remote sensing imagery. These key objectives are achieved through combining methods of probability analysis, uncertainty quantification, evolutionary learning and swarm intelligence, graph theory, image processing, deep learning and feature extraction. Four new techniques have been developed. Firstly, a new data assimilation technique based on statistical distance metrics and probability distribution analysis to achieve a flexible representation of model- and measurement-related uncertainties. Secondly, a method for detecting boundaries of agricultural fields based on remote sensing observations designed to only rely on image-based information in multi-temporal imagery. Thirdly, an improved boundary detection approach based on deep learning techniques and a variety of image features. Fourthly, a new active contours method called Graph-based Growing Contours (GGC) that allows automatized extractionof complex boundary networks from imagery. The new approaches are tested and evaluated on multiple study areas in the states of Schleswig-Holstein, Niedersachsen and Sachsen-Anhalt, Germany, based on combine harvester measurements, cadastral data and manual mappings. All methods were designed with flexibility and applicability in mind. They proved to perform similarly or better than other existing methods and showed potential for large-scale application and their synergetic use. Thanks to low data requirements and flexible use of inputs, their application is neither constrained to the specific applications presented here nor the use of a specific type of sensor or imagery. This flexibility, in theory, enables their use even outside of the field of remote sensing.Landwirtschaftliche Produktivitätssteigerung wurde historisch hauptsächlich durch Erschließung neuer Anbauflächen und später, insbesondere im 19. und 20. Jahrhundert, durch technologische Innovation in Züchtung, Genetik und Agrarchemie sowie Intensivierung in Form von Mechanisierung und Industrialisierung erreicht. In jüngerer Vergangenheit spielen jedoch Informationstechnologie, Digitalisierung und Automatisierung zunehmend eine größere Rolle, um die Produktivität bei reduziertem Umwelteinfluss und Ressourcennutzung weiter zu steigern. Daraus folgen zwei entgegengesetzte Trends: Zum einen Precision Farming, das mithilfe von Detailbeobachtungen die lokale Feldarbeit unterstützt, und zum anderen großskalige landwirtschaftliche Beobachtung von Bestands- und Ertragsmustern zur Analyse makroökonomischer und ökologischer Trends. In beiden Fällen spielen Fernerkundungsdaten eine entscheidende Rolle und gewinnen dank sinkender Kosten und zunehmender Verfügbarkeit, sowohl der Daten als auch der Möglichkeiten zu ihrer Verarbeitung und Analyse, weiter an Bedeutung. Die Verfügbarkeit großer, freier Archive von globaler Abdeckung werden in den kommenden Jahren voraussichtlich zu einer zunehmenden Verwendung führen. Diese Dissertation behandelt mehrere Aspekte der Fernerkundungsanwendung in der Landwirtschaft und präsentiert neue Methoden zu drei Themenbereichen: (1) Assimilation von Fernerkundungsdaten in dynamischen Agrarmodellen; (2) Erkennung von landwirtschaftlichen Feldgrenzen auf Basis von Fernerkundungsbeobachtungen; und (3) Konturextraktion und Erstellung von Polygonen aus Fernerkundungsaufnahmen. Zur Bearbeitung dieser Zielsetzungen werden verschiedene Techniken aus der Wahrscheinlichkeitsanalyse, Unsicherheitsquantifizierung, dem evolutionären Lernen und der Schwarmintelligenz, der Graphentheorie, dem Bereich der Bildverarbeitung, Deep Learning und Feature-Extraktion kombiniert. Es werden vier neue Methoden vorgestellt. Erstens, eine neue Methode zur Datenassimilation basierend auf statistischen Distanzmaßen und Wahrscheinlichkeitsverteilungen zur flexiblen Abbildung von Modell- und Messungenauigkeiten. Zweitens, eine neue Technik zur Erkennung von Feldgrenzen, ausschließlich auf Basis von Bildinformationen aus multi-temporalen Fernerkundungsdaten. Drittens, eine verbesserte Feldgrenzenerkennung basierend auf Deep Learning Methoden und verschiedener Bildmerkmale. Viertens, eine neue Aktive Kontur Methode namens Graph-based Growing Contours (GGC), die es erlaubt, komplexe Netzwerke von Konturen aus Bildern zu extrahieren. Alle neuen Ansätze werden getestet und evaluiert anhand von Mähdreschermessungen, Katasterdaten und manuellen Kartierungen in verschiedenen Testregionen in den Bundesländern Schleswig-Holstein, Niedersachsen und Sachsen-Anhalt. Alle vorgestellten Methoden sind auf Flexibilität und Anwendbarkeit ausgelegt. Im Vergleich zu anderen Methoden zeigten sie vergleichbare oder bessere Ergebnisse und verdeutlichten das Potenzial zur großskaligen Anwendung sowie kombinierter Verwendung. Dank der geringen Anforderungen und der flexiblen Verwendung verschiedener Eingangsdaten ist die Nutzung nicht nur auf die hier beschriebenen Anwendungen oder bestimmte Sensoren und Bilddaten beschränkt. Diese Flexibilität erlaubt theoretisch eine breite Anwendung, auch außerhalb der Fernerkundung

Robust Modular Feature-Based Terrain-Aided Visual Navigation and Mapping

The visual feature-based Terrain-Aided Navigation (TAN) system presented in this thesis addresses the problem of constraining inertial drift introduced into the location estimate of Unmanned Aerial Vehicles (UAVs) in GPS-denied environment. The presented TAN system utilises salient visual features representing semantic or human-interpretable objects (roads, forest and water boundaries) from onboard aerial imagery and associates them to a database of reference features created a-priori, through application of the same feature detection algorithms to satellite imagery. Correlation of the detected features with the reference features via a series of the robust data association steps allows a localisation solution to be achieved with a finite absolute bound precision defined by the certainty of the reference dataset. The feature-based Visual Navigation System (VNS) presented in this thesis was originally developed for a navigation application using simulated multi-year satellite image datasets. The extension of the system application into the mapping domain, in turn, has been based on the real (not simulated) flight data and imagery. In the mapping study the full potential of the system, being a versatile tool for enhancing the accuracy of the information derived from the aerial imagery has been demonstrated. Not only have the visual features, such as road networks, shorelines and water bodies, been used to obtain a position ’fix’, they have also been used in reverse for accurate mapping of vehicles detected on the roads into an inertial space with improved precision. Combined correction of the geo-coding errors and improved aircraft localisation formed a robust solution to the defense mapping application. A system of the proposed design will provide a complete independent navigation solution to an autonomous UAV and additionally give it object tracking capability

Rotate and Hold and Scan (RAHAS): Structured Light Illumination for Use in Remote Areas

As a critical step after the discovery of material culture in the field, archaeologists have a need to document these findings with a slew of different physical measurements and photographs from varying perspectives. 3-D imaging is becoming increasingly popular as the primary documenting method to replace the plethora of tests and measurements, but for remote areas 3-D becomes more cumbersome due to physical and environmental constraints. The difficulty of using a 3-D imaging system in such environments is drastically lessened while using the RAHAS technique, since it acquires scans untethered to a computer. The goal of this thesis is to present the RAHAS Structured Light Illumination technique for 3-D image acquisition, and the performance of the RAHAS technique as a measurement tool for documentation of material culture on a field trip to the Rio Platano Biosphere in Honduras

Two and three dimensional segmentation of multimodal imagery

The role of segmentation in the realms of image understanding/analysis, computer vision, pattern recognition, remote sensing and medical imaging in recent years has been significantly augmented due to accelerated scientific advances made in the acquisition of image data. This low-level analysis protocol is critical to numerous applications, with the primary goal of expediting and improving the effectiveness of subsequent high-level operations by providing a condensed and pertinent representation of image information. In this research, we propose a novel unsupervised segmentation framework for facilitating meaningful segregation of 2-D/3-D image data across multiple modalities (color, remote-sensing and biomedical imaging) into non-overlapping partitions using several spatial-spectral attributes. Initially, our framework exploits the information obtained from detecting edges inherent in the data. To this effect, by using a vector gradient detection technique, pixels without edges are grouped and individually labeled to partition some initial portion of the input image content. Pixels that contain higher gradient densities are included by the dynamic generation of segments as the algorithm progresses to generate an initial region map. Subsequently, texture modeling is performed and the obtained gradient, texture and intensity information along with the aforementioned initial partition map are used to perform a multivariate refinement procedure, to fuse groups with similar characteristics yielding the final output segmentation. Experimental results obtained in comparison to published/state-of the-art segmentation techniques for color as well as multi/hyperspectral imagery, demonstrate the advantages of the proposed method. Furthermore, for the purpose of achieving improved computational efficiency we propose an extension of the aforestated methodology in a multi-resolution framework, demonstrated on color images. Finally, this research also encompasses a 3-D extension of the aforementioned algorithm demonstrated on medical (Magnetic Resonance Imaging / Computed Tomography) volumes