297 research outputs found

    Seguimiento de objetos en video usando contornos activos y bounding boxes

    Get PDF
    El seguimiento de objetos en forma automática a lo largo de una secuencia de imágenes tiene aplicaciones en áreas tan diversas como robótica, animación, sistemas de seguridad o diagnóstico médico. El algoritmo de seguimiento utilizado en este trabajo comienza con la definición de una curva B-Spline que es el área inicial de búsqueda del contorno de un objeto. Luego se consideran una serie de segmentos de rectas normales a esta curva y se aplica algún método de detección de bordes para hallar puntos sobre el contorno a lo largo de las rectas. Para que el algoritmo de seguimiento del objeto sea exitoso es necesario que la estimación inicial sea muy precisa. En este trabajo se presenta un nuevo método estable y eficiente para evitar errores de parametrización al ajustar el contorno del objeto con una curva B-Spline al comienzo del método de seguimiento. Se utiliza una estructura de aceleración para evitar conflictos al estimar el contorno del objeto. El algoritmo modificado se prueba en videos reales y se observan excelentes resultados.The automatic tracking of objects along a sequence of images has applications in different areas as robotics, animation, security systems or medical diagnosis. The tracking algorithm used in this paper starts fitting the contour of an object, using a B-Spline curve as the initial search region. The next step is to sample normal vectors at regularly-spaced points along this curve and to detect points on the border of the object by applying some image-processing filter along the curve normals. A good initial estimate is required for the tracking algorithm to be successful. This paper presents a method to avoid parametrization errors when fitting the outline of the object at the beginning of the tracking. It has the advantage of being simple and efficient. Conflicts when fitting the contour of the object are avoided using an acceleration structure. The modified algorithm is tested against real videos with excellent results.V Workshop de Computación Gráfica, Imágenes Y VisualizaciónRed de Universidades con Carreras en Informática (RedUNCI

    Seguimiento de objetos en video usando contornos activos y bounding boxes

    Get PDF
    El seguimiento de objetos en forma automática a lo largo de una secuencia de imágenes tiene aplicaciones en áreas tan diversas como robótica, animación, sistemas de seguridad o diagnóstico médico. El algoritmo de seguimiento utilizado en este trabajo comienza con la definición de una curva B-Spline que es el área inicial de búsqueda del contorno de un objeto. Luego se consideran una serie de segmentos de rectas normales a esta curva y se aplica algún método de detección de bordes para hallar puntos sobre el contorno a lo largo de las rectas. Para que el algoritmo de seguimiento del objeto sea exitoso es necesario que la estimación inicial sea muy precisa. En este trabajo se presenta un nuevo método estable y eficiente para evitar errores de parametrización al ajustar el contorno del objeto con una curva B-Spline al comienzo del método de seguimiento. Se utiliza una estructura de aceleración para evitar conflictos al estimar el contorno del objeto. El algoritmo modificado se prueba en videos reales y se observan excelentes resultados.The automatic tracking of objects along a sequence of images has applications in different areas as robotics, animation, security systems or medical diagnosis. The tracking algorithm used in this paper starts fitting the contour of an object, using a B-Spline curve as the initial search region. The next step is to sample normal vectors at regularly-spaced points along this curve and to detect points on the border of the object by applying some image-processing filter along the curve normals. A good initial estimate is required for the tracking algorithm to be successful. This paper presents a method to avoid parametrization errors when fitting the outline of the object at the beginning of the tracking. It has the advantage of being simple and efficient. Conflicts when fitting the contour of the object are avoided using an acceleration structure. The modified algorithm is tested against real videos with excellent results.V Workshop de Computación Gráfica, Imágenes Y VisualizaciónRed de Universidades con Carreras en Informática (RedUNCI

    Statistical models in medical image analysis

    Get PDF
    Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2000.Includes bibliographical references (leaves 149-156).Computational tools for medical image analysis help clinicians diagnose, treat, monitor changes, and plan and execute procedures more safely and effectively. Two fundamental problems in analyzing medical imagery are registration, which brings two or more datasets into correspondence, and segmentation, which localizes the anatomical structures in an image. The noise and artifacts present in the scans, combined with the complexity and variability of patient anatomy, limit the effectiveness of simple image processing routines. Statistical models provide application-specific context to the problem by incorporating information derived from a training set consisting of instances of the problem along with the solution. In this thesis, we explore the benefits of statistical models for medical image registration and segmentation. We present a technique for computing the rigid registration of pairs of medical images of the same patient. The method models the expected joint intensity distribution of two images when correctly aligned. The registration of a novel set of images is performed by maximizing the log likelihood of the transformation, given the joint intensity model. Results aligning SPGR and dual-echo magnetic resonance scans demonstrate sub-voxel accuracy and large region of convergence. A novel segmentation method is presented that incorporates prior statistical models of intensity, local curvature, and global shape to direct the segmentation toward a likely outcome. Existing segmentation algorithms generally fit into one of the following three categories: boundary localization, voxel classification, and atlas matching, each with different strengths and weaknesses. Our algorithm unifies these approaches. A higher dimensional surface is evolved based on local and global priors such that the zero level set converges on the object boundary. Results segmenting images of the corpus callosum, knee, and spine illustrate the strength and diversity of this approach.by Michael Emmanuel Leventon.Ph.D

    Advanced Knowledge Application in Practice

    Get PDF
    The integration and interdependency of the world economy leads towards the creation of a global market that offers more opportunities, but is also more complex and competitive than ever before. Therefore widespread research activity is necessary if one is to remain successful on the market. This book is the result of research and development activities from a number of researchers worldwide, covering concrete fields of research

    Automatic segmentation of the human thigh muscles in magnetic resonance imaging

    Get PDF
    Advances in magnetic resonance imaging (MRI) and analysis techniques have improved diagnosis and patient treatment pathways. Typically, image analysis requires substantial technical and medical expertise and MR images can su↵er from artefacts, echo and intensity inhomogeneity due to gradient pulse eddy currents and inherent e↵ects of pulse radiation on MRI radio frequency (RF) coils that complicates the analysis. Processing and analysing serial sections of MRI scans to measure tissue volume is an additional challenge as the shapes and the borders between neighbouring tissues change significantly by anatomical location. Medical imaging solutions are needed to avoid laborious manual segmentation of specified regions of interest (ROI) and operator errors. The work set out in this thesis has addressed this challenge with a specific focus on skeletal muscle segmentation of the thigh. The aim was to develop an MRI segmentation framework for the quadriceps muscles, femur and bone marrow. Four contributions of this research include: (1) the development of a semi-automatic segmentation framework for a single transverse-plane image; (2) automatic segmentation of a single transverseplane image; (3) the automatic segmentation of multiple contiguous transverse-plane images from a full MRI thigh scan; and (4) the use of deep learning for MRI thigh quadriceps segmentation. Novel image processing, statistical analysis and machine learning algorithms were developed for all solutions and they were compared against current gold-standard manual segmentation. Frameworks (1) and (3) require minimal input from the user to delineate the muscle border. Overall, the frameworks in (1), (2) and (3) o↵er very good output performance, with respective framework’s mean segmentation accuracy by JSI and processing time of: (1) 0.95 and 17 sec; (2) 0.85 and 22 sec; and (3) 0.93 and 3 sec. For the framework in (4), the ImageNet trained model was customized by replacing the fully-connected layers in its architecture to convolutional layers (hence the name of Fully Convolutional Network (FCN)) and the pre-trained model was transferred for the ROI segmentation task. With the implementation of post-processing for image filtering and morphology to the segmented ROI, we have successfully accomplished a new benchmark for thigh MRI analysis. The mean accuracy and processing time with this framework are 0.9502 (by JSI ) and 0.117 sec per image, respectively

    AUGMENTED REALITY AND INTRAOPERATIVE C-ARM CONE-BEAM COMPUTED TOMOGRAPHY FOR IMAGE-GUIDED ROBOTIC SURGERY

    Get PDF
    Minimally-invasive robotic-assisted surgery is a rapidly-growing alternative to traditionally open and laparoscopic procedures; nevertheless, challenges remain. Standard of care derives surgical strategies from preoperative volumetric data (i.e., computed tomography (CT) and magnetic resonance (MR) images) that benefit from the ability of multiple modalities to delineate different anatomical boundaries. However, preoperative images may not reflect a possibly highly deformed perioperative setup or intraoperative deformation. Additionally, in current clinical practice, the correspondence of preoperative plans to the surgical scene is conducted as a mental exercise; thus, the accuracy of this practice is highly dependent on the surgeon’s experience and therefore subject to inconsistencies. In order to address these fundamental limitations in minimally-invasive robotic surgery, this dissertation combines a high-end robotic C-arm imaging system and a modern robotic surgical platform as an integrated intraoperative image-guided system. We performed deformable registration of preoperative plans to a perioperative cone-beam computed tomography (CBCT), acquired after the patient is positioned for intervention. From the registered surgical plans, we overlaid critical information onto the primary intraoperative visual source, the robotic endoscope, by using augmented reality. Guidance afforded by this system not only uses augmented reality to fuse virtual medical information, but also provides tool localization and other dynamic intraoperative updated behavior in order to present enhanced depth feedback and information to the surgeon. These techniques in guided robotic surgery required a streamlined approach to creating intuitive and effective human-machine interferences, especially in visualization. Our software design principles create an inherently information-driven modular architecture incorporating robotics and intraoperative imaging through augmented reality. The system's performance is evaluated using phantoms and preclinical in-vivo experiments for multiple applications, including transoral robotic surgery, robot-assisted thoracic interventions, and cocheostomy for cochlear implantation. The resulting functionality, proposed architecture, and implemented methodologies can be further generalized to other C-arm-based image guidance for additional extensions in robotic surgery
    • …
    corecore