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 149 for “"Medical Image"”.
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Medical image enhancement
Each image acquired from a medical imaging system is often part of a two-dimensional (2-D) image set whose total presents a three-dimensional (3-D) object for diagnosis. Unfortunately, sometimes these images are of poor quality. These distortions cause an inadequate object-of-interest presentation, …
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Language-Centric Medical Image Understanding
This thesis advances medical image understanding by leveraging the multifaceted roles of language: as supervision, prior knowledge, and a medium for communication. We introduce three main contributions: (1) a weakly supervised framework that uses language in clinical reports to guide fine-grained …
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Semi-automatic medical image segmentation
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, February 2002.
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Computational methods for medical image registration
… to the improvement of techniques in the field of medical image analysis. Imaging modalities have been improved and new acquisition methods have been introduced to reveal greater anatomical detail and to allow for more information to be extracted from medical images. However, certain challenges …
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Text Prompt-Driven Medical Image Segmentation
Medical image segmentation plays a crucial role in accurate diagnosis, treatment planning, and surgical navigation by precisely identifying pathological regions. However, traditional segmentation methods typically rely on dense pixel-level annotations and heavy computational resources, which pose …
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Intercomparison of medical image segmentation algorithms
… are several stages involved in analyzing an MRI image, segmentation being one of the most important. Image segmentation is essentially the process of identifying and classifying the constituent parts of an image, and is usually very complex. Unfortunately, it suffers from artefacts including …
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Curve evolution for medical image segmentation
Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2000.
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Statistical models in medical image analysis
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 …
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Hierarchical three-dimensional medical image registration
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1997.
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Machine Learning towards General Medical Image Segmentation
… regression (MSVR) on head and neck (HaN) CT images. Subsequently, we incorporated multiplane and multimodality spinal images and presented the first deep learning multiapplication framework for shape regression, the holistic multitask regression network (HMR-Net). MSVR and HMR-Net's …
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Anthropomorphic Model for Medical Image Quality Assessment
… dissertation explores advancements in task-based image quality assessment through the development and evaluation of an anthropomorphic visual search model observer. The model incorporates a novel threshold mechanism inspired by human visual system principles, particularly emphasizing the selective …
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Medical Image Registration Using Artificial Neural Network
<p>Image registration is the transformation of different sets of images into one coordinate system in order to align and overlay multiple images. Image registration is used in many fields such as medical imaging, remote sensing, and computer vision. It is very important in medical research, where …
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Multimodal Representation Learning for Medical Image Analysis
… that exploit multimodal clinical data to improve medical image analysis. Medical images capture rich information of a patient’s physiological and disease status, central in clinical practice and research. Computational models, such as artificial neural networks, enable automatic and quantitative …
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Model based three dimensional medical image segmentation
Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1999.
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Learning with imperfect datasets in medical image segmentation
Medical image segmentation partitions medical images into distinct physiological regions, such as organs and lesions, essential for diagnosis and treatment planning. Deep neural networks have advanced this field recently, yet real-world performance remains unsatisfactory due to imperfect data and …
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Tissue segmentation using medical image processing chain optimization
… and optimize parameters of a full sequence of image processing chain. This thesis presents a universal algorithm that uses three images and their corresponding gold images to train the framework. The optimization algorithm explores the search space for the best sequence of the image processing …
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MEDICAL IMAGE BASED PREOPERATIVE ANALYSIS AND INTRAOPERATIVE NAVIGATION
In recent years, medical image processing, artificial intelligence, computer-assisted system and robotic intervention are gradually applied to medicine to help surgeons for medical image analysis and surgical navigation, promoting the vigorous progress of precision medicine. This thesis mainly …
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Deep neural networks for medical image super-resolution
Super-resolution plays an essential role in medical imaging because it provides an alternative way to achieve high spatial resolutions with no extra acquisition cost. In the past decades, the rapid development of deep neural networks has ensured high reconstruction fidelity and photo-realistic …
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