Automatic segmentation of the knee bones using 3D active shape models

This paper presents an automated segmentation approach for MR images of the knee bones. The bones are the first stage of a segmentation system for the knee, primarily aimed at the automated segmentation of the cartilages. The segmentation is performed using 3D active shape models (ASM), which are initialized using an affine registration to an atlas. The 3D ASMs of the bones are created automatically using a point distribution model optimization scheme. The accuracy and robustness of the segmentation approach was experimentally validated using an MR database of fat suppressed spoiled gradient recall images.

Integration of a 3D hydrogel matrix within a hollow core photonic crystal fibre for DNA probe immobilization

In this paper, we demonstrate the integration of a 3D hydrogel matrix within a hollow core photonic crystal fibre (HC-PCF). In addition, we also show the fluorescence of Cy5-labelled DNA molecules immobilized within the hydrogel formed in two different types of HC-PCF. The 3D hydrogel matrix is designed to bind with the amino groups of biomolecules using an appropriate cross-linker, providing higher sensitivity and selectivity than the standard 2D coverage, enabling a greater number of probe molecules to be available per unit area. The HC-PCFs, on the other hand, can be designed to maximize the capture of fluorescence to improve sensitivity and provide longer interaction lengths. This could enable the development of fibre-based point-of-care and remote systems, where the enhanced sensitivity would relax the constraints placed on sources and detectors. In this paper, we will discuss the formation of such polyethylene glycol diacrylate (PEGDA) hydrogels within a HC-PCF, including their optical properties such as light propagation and auto-fluorescence.

Live 3D shape reconstruction, recognition and registration

We present a video-based system which interactively captures the geometry of a 3D object in the form of a point cloud, then recognizes and registers known objects in this point cloud in a matter of seconds (fig. 1). In order to achieve interactive speed, we exploit both efficient inference algorithms and parallel computation, often on a GPU. The system can be broken down into two distinct phases: geometry capture, and object inference. We now discuss these in further detail. © 2011 IEEE.

Rigid and non-rigid point-based medical image registration

The primary goal of this dissertation is to develop point-based rigid and non-rigid image registration methods that have better accuracy than existing methods. We first present point-based PoIRe, which provides the framework for point-based global rigid registrations. It allows a choice of different search strategies including (a) branch-and-bound, (b) probabilistic hill-climbing, and (c) a novel hybrid method that takes advantage of the best characteristics of the other two methods. We use a robust similarity measure that is insensitive to noise, which is often introduced during feature extraction. We show the robustness of PoIRe using it to register images obtained with an electronic portal imaging device (EPID), which have large amounts of scatter and low contrast. To evaluate PoIRe we used (a) simulated images and (b) images with fiducial markers; PoIRe was extensively tested with 2D EPID images and images generated by 3D Computer Tomography (CT) and Magnetic Resonance (MR) images. PoIRe was also evaluated using benchmark data sets from the blind retrospective evaluation project (RIRE). We show that PoIRe is better than existing methods such as Iterative Closest Point (ICP) and methods based on mutual information. We also present a novel point-based local non-rigid shape registration algorithm. We extend the robust similarity measure used in PoIRe to non-rigid registrations adapting it to a free form deformation (FFD) model and making it robust to local minima, which is a drawback common to existing non-rigid point-based methods. For non-rigid registrations we show that it performs better than existing methods and that is less sensitive to starting conditions. We test our non-rigid registration method using available benchmark data sets for shape registration. Finally, we also explore the extraction of features invariant to changes in perspective and illumination, and explore how they can help improve the accuracy of multi-modal registration. For multimodal registration of EPID-DRR images we present a method based on a local descriptor defined by a vector of complex responses to a circular Gabor filter.

Rigid and Non-rigid Point-based Medical Image Registration

The primary goal of this dissertation is to develop point-based rigid and non-rigid image registration methods that have better accuracy than existing methods. We first present point-based PoIRe, which provides the framework for point-based global rigid registrations. It allows a choice of different search strategies including (a) branch-and-bound, (b) probabilistic hill-climbing, and (c) a novel hybrid method that takes advantage of the best characteristics of the other two methods. We use a robust similarity measure that is insensitive to noise, which is often introduced during feature extraction. We show the robustness of PoIRe using it to register images obtained with an electronic portal imaging device (EPID), which have large amounts of scatter and low contrast. To evaluate PoIRe we used (a) simulated images and (b) images with fiducial markers; PoIRe was extensively tested with 2D EPID images and images generated by 3D Computer Tomography (CT) and Magnetic Resonance (MR) images. PoIRe was also evaluated using benchmark data sets from the blind retrospective evaluation project (RIRE). We show that PoIRe is better than existing methods such as Iterative Closest Point (ICP) and methods based on mutual information. We also present a novel point-based local non-rigid shape registration algorithm. We extend the robust similarity measure used in PoIRe to non-rigid registrations adapting it to a free form deformation (FFD) model and making it robust to local minima, which is a drawback common to existing non-rigid point-based methods. For non-rigid registrations we show that it performs better than existing methods and that is less sensitive to starting conditions. We test our non-rigid registration method using available benchmark data sets for shape registration. Finally, we also explore the extraction of features invariant to changes in perspective and illumination, and explore how they can help improve the accuracy of multi-modal registration. For multimodal registration of EPID-DRR images we present a method based on a local descriptor defined by a vector of complex responses to a circular Gabor filter.

The application of 3D printing to study microfluidic architecture for 'on-chip' mixing systems for SRCD and UV spectroscopy

This paper details methodologies that have been explored for the fast proofing of on-chip architectures for Circular Dichroism techniques. Flow-cell devices fabricated from UV transparent Quartz are used for these experiments. The complexity of flow-cell production typically results in lead times of six months from order to delivery. Only at that point can the on-chip architecture be tested empirically and any required modifications determined ready for the next six month iteration phase. By using the proposed 3D printing and PDMS moulding techniques for fast proofing on-chip architectures the optimum design can be determined within a matter of hours prior to commitment to quartz chip production.

Advanced Applications of 3D Dosimetry and 3D Printing in Radiation Therapy

As complex radiotherapy techniques become more readily-practiced, comprehensive 3D dosimetry is a growing necessity for advanced quality assurance. However, clinical implementation has been impeded by a wide variety of factors, including the expense of dedicated optical dosimeter readout tools, high operational costs, and the overall difficulty of use. To address these issues, a novel dry-tank optical CT scanner was designed for PRESAGE 3D dosimeter readout, relying on 3D printed components and omitting costly parts from preceding optical scanners. This work details the design, prototyping, and basic commissioning of the Duke Integrated-lens Optical Scanner (DIOS).

The convex scanning geometry was designed in ScanSim, an in-house Monte Carlo optical ray-tracing simulation. ScanSim parameters were used to build a 3D rendering of a convex ‘solid tank’ for optical-CT, which is capable of collimating a point light source into telecentric geometry without significant quantities of refractive-index matched fluid. The model was 3D printed, processed, and converted into a negative mold via rubber casting to produce a transparent polyurethane scanning tank. The DIOS was assembled with the solid tank, a 3W red LED light source, a computer-controlled rotation stage, and a 12-bit CCD camera. Initial optical phantom studies show negligible spatial inaccuracies in 2D projection images and 3D tomographic reconstructions. A PRESAGE 3D dose measurement for a 4-field box treatment plan from Eclipse shows 95% of voxels passing gamma analysis at 3%/3mm criteria. Gamma analysis between tomographic images of the same dosimeter in the DIOS and DLOS systems show 93.1% agreement at 5%/1mm criteria. From this initial study, the DIOS has demonstrated promise as an economically-viable optical-CT scanner. However, further improvements will be necessary to fully develop this system into an accurate and reliable tool for advanced QA.

Pre-clinical animal studies are used as a conventional means of translational research, as a midpoint between in-vitro cell studies and clinical implementation. However, modern small animal radiotherapy platforms are primitive in comparison with conventional linear accelerators. This work also investigates a series of 3D printed tools to expand the treatment capabilities of the X-RAD 225Cx orthovoltage irradiator, and applies them to a feasibility study of hippocampal avoidance in rodent whole-brain radiotherapy.

As an alternative material to lead, a novel 3D-printable tungsten-composite ABS plastic, GMASS, was tested to create precisely-shaped blocks. Film studies show virtually all primary radiation at 225 kVp can be attenuated by GMASS blocks of 0.5cm thickness. A state-of-the-art software, BlockGen, was used to create custom hippocampus-shaped blocks from medical image data, for any possible axial treatment field arrangement. A custom 3D printed bite block was developed to immobilize and position a supine rat for optimal hippocampal conformity. An immobilized rat CT with digitally-inserted blocks was imported into the SmART-Plan Monte-Carlo simulation software to determine the optimal beam arrangement. Protocols with 4 and 7 equally-spaced fields were considered as viable treatment options, featuring improved hippocampal conformity and whole-brain coverage when compared to prior lateral-opposed protocols. Custom rodent-morphic PRESAGE dosimeters were developed to accurately reflect these treatment scenarios, and a 3D dosimetry study was performed to confirm the SmART-Plan simulations. Measured doses indicate significant hippocampal sparing and moderate whole-brain coverage.

Two devices, each of which measure water velocity; on the right is an acoustic device that is permanently maintained at Gumbo Limbo by PI Rene Price; on the left is a 3D camera with a long fiberoptic lens, deployed for testing purposes by collaborator Evan Variano.

http://digitalcommons.fiu.edu/fce_lter_photos/1303/thumbnail.jpg

Bin picking de objetos polimórficos convexos usando perceção 3D

O bin picking é um processo de grande interesse na indústria, uma vez que permite maior automatização, aumento da capacidade de produção e redução dos custos. Este tem vindo a evoluir bastante ao longo dos anos e essa evolução fez com que sistemas de perceção 3D começassem a ser implementados. Este trabalho tem como principal objetivo desenvolver um sistema de bin picking usando apenas perceção 3D. O sistema deve ser capaz de determinar a posição e orientação de objetos com diferentes formas e tamanhos, posicionados aleatoriamente numa superfície de trabalho. Os objetos utilizados para fazer os testes experimentais, são esferas, cilindros e prismas, uma vez que abrangem as formas geométricas existentes em muitos produtos submetidos a bin picking. Após a identi cação e seleção do objeto a apanhar, o manipulador deve autonomamente posicionar-se para fazer a aproximação e recolha do mesmo. A aquisição de dados é feita através de uma câmara Kinect. Dos dados recebidos apenas são trabalhados os referentes à profundidade, centrando-se assim este trabalho na análise e tratamento de nuvem de pontos. O sistema desenvolvido cumpre com os objetivos estabelecidos. Consegue localizar e apanhar objetos em várias posições e orientações. Além disso apresenta uma velocidade de processamento compatível com a aplicação em causa.

Reimaginar el mito de las gucamayas ilustraciones 3D: transición de las guacamayas de seres mágicos a mujeres

El siguiente proyecto, es una propuesta gráfica ilustrada en 3D, basada en la mitología cañari. El estudio realizado esboza breves rasgos de la cosmovisión generada a través del mito de la guacamaya. Donde se revela datos importantes que construye la identidad cultural del pueblo cañari. De igual manera se refleja la ilustración como proceso de creación de conocimientos, donde se comprende y exponen los contextos de la imagen dados desde el estudio anatómico y el uso de programas orientados a la generación de imágenes 2d y 3d. Asimismo se refleja la interpretación del mito expuesto desde la mirada del autor, desplegando potencias particulares que redefinen las practicas tradicionales, permitiendo que la ilustración se piense así misma como una producción y no solo como representación

POSE ESTIMATION AND 3D RECONSTRUCTION USING SENSOR FUSION

A camera maps 3-dimensional (3D) world space to a 2-dimensional (2D) image space. In the process it loses the depth information, i.e., the distance from the camera focal point to the imaged objects. It is impossible to recover this information from a single image. However, by using two or more images from different viewing angles this information can be recovered, which in turn can be used to obtain the pose (position and orientation) of the camera. Using this pose, a 3D reconstruction of imaged objects in the world can be computed. Numerous algorithms have been proposed and implemented to solve the above problem; these algorithms are commonly called Structure from Motion (SfM). State-of-the-art SfM techniques have been shown to give promising results. However, unlike a Global Positioning System (GPS) or an Inertial Measurement Unit (IMU) which directly give the position and orientation respectively, the camera system estimates it after implementing SfM as mentioned above. This makes the pose obtained from a camera highly sensitive to the images captured and other effects, such as low lighting conditions, poor focus or improper viewing angles. In some applications, for example, an Unmanned Aerial Vehicle (UAV) inspecting a bridge or a robot mapping an environment using Simultaneous Localization and Mapping (SLAM), it is often difficult to capture images with ideal conditions. This report examines the use of SfM methods in such applications and the role of combining multiple sensors, viz., sensor fusion, to achieve more accurate and usable position and reconstruction information. This project investigates the role of sensor fusion in accurately estimating the pose of a camera for the application of 3D reconstruction of a scene. The first set of experiments is conducted in a motion capture room. These results are assumed as ground truth in order to evaluate the strengths and weaknesses of each sensor and to map their coordinate systems. Then a number of scenarios are targeted where SfM fails. The pose estimates obtained from SfM are replaced by those obtained from other sensors and the 3D reconstruction is completed. Quantitative and qualitative comparisons are made between the 3D reconstruction obtained by using only a camera versus that obtained by using the camera along with a LIDAR and/or an IMU. Additionally, the project also works towards the performance issue faced while handling large data sets of high-resolution images by implementing the system on the Superior high performance computing cluster at Michigan Technological University.

3D Surveying and Data Management towards the Realization of a Knowledge System for Cultural Heritage

The research activities involved the application of the Geomatic techniques in the Cultural Heritage field, following the development of two themes: Firstly, the application of high precision surveying techniques for the restoration and interpretation of relevant monuments and archaeological finds. The main case regards the activities for the generation of a high-fidelity 3D model of the Fountain of Neptune in Bologna. In this work, aimed to the restoration of the manufacture, both the geometrical and radiometrical aspects were crucial. The final product was the base of a 3D information system representing a shared tool where the different figures involved in the restoration activities shared their contribution in a multidisciplinary approach. Secondly, the arrangement of 3D databases for a Building Information Modeling (BIM) approach, in a process which involves the generation and management of digital representations of physical and functional characteristics of historical buildings, towards a so-called Historical Building Information Model (HBIM). A first application was conducted for the San Michele in Acerboli’s church in Santarcangelo di Romagna. The survey was performed by the integration of the classical and modern Geomatic techniques and the point cloud representing the church was used for the development of a HBIM model, where the relevant information connected to the building could be stored and georeferenced. A second application regards the domus of Obellio Firmo in Pompeii, surveyed by the integration of the classical and modern Geomatic techniques. An historical analysis permitted the definitions of phases and the organization of a database of materials and constructive elements. The goal is the obtaining of a federate model able to manage the different aspects: documental, analytic and reconstructive ones.

Optimized methods to digital survey and 3D modelling of cultural heritage assets

Currently making digital 3D models and replicas of the cultural heritage assets play an important role in the preservation and having a high detail source for future research and intervention. In this dissertation, it is tried to assess different methods for digital surveying and making 3D replicas of cultural heritage assets in different scales of size. The methodologies vary in devices, software, workflow, and the amount of skill that is required. The three phases of the 3D modelling process are data acquisition, modelling, and model presentation. Each of these sections is divided into sub-sections and there are several approaches, methods, devices, and software that may be employed, furthermore, the selection process should be based on the operation's goal, available facilities, the scale and properties of the object or structure to be modeled, as well as the operators' expertise and experience. The most key point to remember is that the 3D modelling operation should be properly accurate, precise, and reliable; therefore, there are so many instructions and pieces of advice on how to perform 3D modelling effectively. It is an attempt to compare and evaluate the various ways of each phase in order to explain and demonstrate their differences, benefits, and drawbacks in order to serve as a simple guide for new and/or inexperienced users.

An Embedded In-Cabin Lightweight High-Performance 3D Gaze Estimation System

Gaze estimation has gained interest in recent years for being an important cue to obtain information about the internal cognitive state of humans. Regardless of whether it is the 3D gaze vector or the point of gaze (PoG), gaze estimation has been applied in various fields, such as: human robot interaction, augmented reality, medicine, aviation and automotive. In the latter field, as part of Advanced Driver-Assistance Systems (ADAS), it allows the development of cutting-edge systems capable of mitigating road accidents by monitoring driver distraction. Gaze estimation can be also used to enhance the driving experience, for instance, autonomous driving. It also can improve comfort with augmented reality components capable of being commanded by the driver's eyes. Although, several high-performance real-time inference works already exist, just a few are capable of working with only a RGB camera on computationally constrained devices, such as a microcontroller. This work aims to develop a low-cost, efficient and high-performance embedded system capable of estimating the driver's gaze using deep learning and a RGB camera. The proposed system has achieved near-SOTA performances with about 90% less memory footprint. The capabilities to generalize in unseen environments have been evaluated through a live demonstration, where high performance and near real-time inference were obtained using a webcam and a Raspberry Pi4.

AGV Safety Areas Obstacle Detection and Interaction with Point Cloud Derived Map

This thesis project aims to the development of an algorithm for the obstacle detection and the interaction between the safety areas of an Automated Guided Vehicles (AGV) and a Point Cloud derived map inside the context of a CAD software. The first part of the project focuses on the implementation of an algorithm for the clipping of general polygons, with which has been possible to: construct the safety areas polygon, derive the sweep of this areas along the navigation path performing a union and detect the intersections with line or polygon representing the obstacles. The second part is about the construction of a map in terms of geometric entities (lines and polygons) starting from a point cloud given by the 3D scan of the environment. The point cloud is processed using: filters, clustering algorithms and concave/convex hull derived algorithms in order to extract line and polygon entities representing obstacles. Finally, the last part aims to use the a priori knowledge of possible obstacle detections on a given segment, to predict the behavior of the AGV and use this prediction to optimize the choice of the vehicle's assigned velocity in that segment, minimizing the travel time.