Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 34 for “"Automated segmentation"”.
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Automated segmentation, detection and fitting of piping elements from terrestrial LIDAR data
… with an unprecedented level of details. However, automated exploitation of LIDAR data is challenging, due to the non-uniform spatial sampling of the point clouds as well as to the massive volumes of data, which may range from a few million points to hundreds of millions of points depending on the …
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Graph Theory and Dynamic Programming Framework for Automated Segmentation of Ophthalmic Imaging Biomarkers
… and fluid-filled regions as a guide for GTDP segmentation.</p><p>The development of fast and accurate segmentation algorithms based on the GTDP framework has significantly reduced the time and resources necessary to conduct large-scale, multi-center clinical trials. This is one step closer …
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Automated segmentation of radiodense tissue in digitized mammograms using a constrained Neyman-Pearson classifier
… design, development and validation of a novel automated algorithm for estimating the percentage of radiodense tissue in a digitized mammogram. The technique involves determining a dynamic threshold for segmenting radiodense indications in mammograms. Both the mammographic image and the …
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Spatially varying threshold models for the automated segmentation of radiodense tissue in digitized mammograms
… risk of breast cancer. This thesis presents an automated method to quantify the amount of radiodense tissue found in a digitized mammogram. The algorithm employs a radial basis function neural network in order to segment the breast tissue region from the remainder of the X-ray. A spatially …
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Quantitative Texture Analysis and Automated Segmentation of Patellar Tendon Sonographic Images on Collegiate Athletes
… convolutional neural networks to automate image segmentations for determination of quantitative measures to determine injury. The primary objective of this research is to identify texture parameters the distinguish pre-season from post-season states. Additionally, this study aims to partially …
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Fully Automated Segmentation of High Grade Serous Ovarian Cancer on Computed Tomography Images using Deep Learning
… deep neural networks can be used for the fully automated segmentation of high grade serous ovarian cancer. The recent rise of deep learning has pushed the limits of what algorithms can achieve in fields of image analysis, such as the task of segmentation. The field of medical image segmentation …
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Automated Segmentation and Analysis of High-Speed Video Phase-Detection Data for Boiling Heat Transfer Characterization Using U-Net Convolutional Neural Networks and Uncertainty Quantification
… This thesis presents a novel approach for the automated segmentation and analysis of HSV phase-detection images using U-Net Convolutional Neural Networks (CNNs) and uncertainty quantification techniques. The proposed methodology involves the development of specialized U-Net CNN models for …
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Cardiac MRI Data Segmentation Using the Partial Differential Equation of Allen–Cahn Type
The work deals with segmentation of image data using the algo- rithm based on numerical solution of the geometrical evolution partial differen- tial equation of the Allen-Cahn type. This equation has origin in the description of motion by mean curvature and has diffusive character. The diffusion …
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Differences in callosal and subcortical volumes and associated neurobehavioural deficits in children with prenatal alcohol exposure
… but is time consuming and labour intensive. Automated segmentation programmes, such as FreeSurfer, are a faster alternative. The challenge is creating automated programmes that can provide results that are comparable to manual tracing, especially in a clinical sample. The aims of this thesis …
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Fast and Robust Automatic Segmentation Methods for MR Images of Injured and Cancerous Tissues
… subject to bias. The development of automatic segmentation techniques that make use of robust statistical methods allows for fast and unbiased analysis of MR images.</p><p>In this dissertation, I propose segmentation methods that fall into two classes---(a) segmentation via optimization of a …
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Segmentation of brain x-ray CT images using seeded region growing
… volume extraction, noise suppression and automated segmentation of X-Ray Computerized Tomography (CT) images. The segmentation scheme is based on a Seeded Region Growing algorithm. The intracranial volume extraction is based on image symmetry and the noise suppression filter is based on …
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Bayesian level sets and texture models for image segmentation and classification with application to non-invasive stem cell monitoring
Image segmentation and classification, the identification and demarcation of regions of interest within an image, is necessary prior to subsequent information extraction, analysis, and inference. Many available segmentation algorithms require manual delineation of initial conditions to achieve …
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Error Resilient Video Coding Using Bitstream Syntax And Iterative Microscopy Image Segmentation
… Due to the size and complexity of the images, automated segmentation methods are required to obtain quantitative, objective and reproducible measurements of biological entities. In this thesis, we present two techniques for microscopy image analysis. Our first method, “Jelly Filling” is …
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Segmentation, co-registration, and correlation of optical coherence tomography and X-ray images for breast cancer diagnostics
… traditional imaging modalities. Furthermore, an automated segmentation algorithm would be able to identify tumor areas within the operating room, allowing the surgeon to isolate and remove all tumor tissue.
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NEW ADVANCES IN QUANTITATIVE RADIOLOGY:MRI IMAGING IN MYOPATHIES
… or histologically confirmed myopathies. Manual segmentation and PyRadiomics feature extraction enabled unsupervised clustering, revealing two distinct radiomic patterns corresponding to different clinical phenotypes. The study demonstrates that volumetric radiomics can identify …
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Krūties vėžio magnetinio rezonanso vaizdo tekstūros analizė /
… and those without. The process involved the segmentation of tumors on magnetic resonance images and the extraction of radiomic features using “Olea medical” software. This was followed by a statistical analysis – Mutual information analysis – using Python programming tools. Mutual information …
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Quantitative Magnetic Resonance Imaging and Analysis of Articular Cartilage and Osteoarthritis
… of cartilage is required. This delineation (or segmentation) of cartilage is laborious and time-consuming as it is usually performed manually by an expert observer. Many new advances in image analysis, particularly those in convolutional neural networks (CNNs) and deep learning, have enabled a …
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Collaborative deep reinforcement framework for multimodality integration and learning
… Although deep learning methods have improved automated segmentation and identification, most existing systems operate as static and isolated models, limiting their ability to adapt, collaborate, and improve once deployed in clinical environments. This dissertation develops and evaluates a …
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Deep Learning for Semi-Automated Brain Claustrum Segmentation on Magnetic Resonance (MR) Images
… vision tasks including image classification, segmentation, localization, and annotation. Convolutional Neural Network (CNN) models in DL have been applied to prevention, detection, and diagnosis in predictive medicine. Image segmentation plays a significant role in predictive medicine. …
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Multi-visualization and hybrid segmentation approaches within telemedicine framework
… medical image visualization and image segmentation. And these methods are also demonstrated by the demo software that I developed. One of my research point focuses on medical information storage standard in telemedicine, for example DICOM, which is the predominant standard for the …
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