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Full-Text Articles in Engineering
Scatter Reduction By Exploiting Behaviour Of Convolutional Neural Networks In Frequency Domain, Carlos Ivan Jerez Gonzalez
Scatter Reduction By Exploiting Behaviour Of Convolutional Neural Networks In Frequency Domain, Carlos Ivan Jerez Gonzalez
Theses and Dissertations
In X-ray imaging, scattered radiation can produce a number of artifacts that greatly
undermine the image quality. There are hardware solutions, such as anti-scatter grids.
However, they are costly. A software-based solution is a better option because it is
cheaper and can achieve a higher scatter reduction. Most of the current software-based
approaches are model-based. The main issues with them are the lack of flexibility, expressivity, and the requirement of a model. In consideration of this, we decided to apply
Convolutional Neural Networks (CNNs), since they do not have any of the previously
mentioned issues.
In our approach we split …
Model Augmented Deep Neural Networks For Medical Image Reconstruction Problems, Hongquan Zuo
Model Augmented Deep Neural Networks For Medical Image Reconstruction Problems, Hongquan Zuo
Theses and Dissertations
Solving an ill-posed inverse problem is difficult because it doesn't have a unique solution. In practice, for some important inverse problems, the conventional methods, e.g. ordinary least squares and iterative methods, cannot provide a good estimate. For example, for single image super-resolution and CT reconstruction, the results of these conventional methods cannot satisfy the requirements of these applications. While having more computational resources and high-quality data, researchers try to use machine-learning-based methods, especially deep learning to solve these ill-posed problems. In this dissertation, a model augmented recursive neural network is proposed as a general inverse problem method to solve these …
Machine Intelligence For Advanced Medical Data Analysis: Manifold Learning Approach, Fereshteh S Bashiri
Machine Intelligence For Advanced Medical Data Analysis: Manifold Learning Approach, Fereshteh S Bashiri
Theses and Dissertations
In the current work, linear and non-linear manifold learning techniques, specifically Principle Component Analysis (PCA) and Laplacian Eigenmaps, are studied in detail. Their applications in medical image and shape analysis are investigated.
In the first contribution, a manifold learning-based multi-modal image registration technique is developed, which results in a unified intensity system through intensity transformation between the reference and sensed images. The transformation eliminates intensity variations in multi-modal medical scans and hence facilitates employing well-studied mono-modal registration techniques. The method can be used for registering multi-modal images with full and partial data.
Next, a manifold learning-based scale invariant global shape …
Communications Using Deep Learning Techniques, Priti Gopal Pachpande
Communications Using Deep Learning Techniques, Priti Gopal Pachpande
Legacy Theses & Dissertations (2009 - 2024)
Deep learning (DL) techniques have the potential of making communication systems