Search for dissertations about: "medical image registration"

Showing result 1 - 5 of 55 swedish dissertations containing the words medical image registration.

2. 1. Methods for Reliable Image Registration : Algorithms, Distance Measures, and Representations

   Author : Johan Öfverstedt; Natasa Sladoje; Mattias Heinrich; Uppsala universitet; []
   Keywords : NATURVETENSKAP; NATURAL SCIENCES; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Image registration; alignment; local optimization; global optimization; mutual information; normalized gradient fields; representation learning; Computerized Image Processing; Datoriserad bildbehandling;

   Abstract : Much biomedical and medical research relies on the collection of ever-larger amounts of image data (both 2D images and 3D volumes, as well as time-series) and increasingly from multiple sources. Image registration, the process of finding correspondences between images based on the affinity of features of interest, is often required as a vital step towards the final analysis, which may consist of a comparison of images, measurement of movement, or fusion of complementary information. READ MORE

4. 2. Representation Learning and Information Fusion : Applications in Biomedical Image Processing

   Author : Elisabeth Wetzer; Nataša Sladoje; Fred Hamprecht; Uppsala universitet; []
   Keywords : NATURVETENSKAP; NATURAL SCIENCES; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Representation Learning; Texture Descriptors; Equivariant Neural Networks; Contrastive Learning; Image Classification; Image Registration; Image Retrieval; Digital Pathology; Computerized Image Processing; Datoriserad bildbehandling;

   Abstract : In recent years Machine Learning and in particular Deep Learning have excelled in object recognition and classification tasks in computer vision. As these methods extract features from the data itself by learning features that are relevant for a particular task, a key aspect of this remarkable success is the amount of data on which these methods train. READ MORE

5. 3. Combining Shape and Learning for Medical Image Analysis

   Author : Jennifer Alvén; Chalmers tekniska högskola; []
   Keywords : NATURVETENSKAP; NATURAL SCIENCES; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; feature-based registration; convolutional neural networks; conditional random fields; medical image segmentation; random decision forests; machine learning; multi-atlas segmentation; medical image registration; shape models;

   Abstract : Automatic methods with the ability to make accurate, fast and robust assessments of medical images are highly requested in medical research and clinical care. Excellent automatic algorithms are characterized by speed, allowing for scalability, and an accuracy comparable to an expert radiologist. READ MORE

6. 4. Image Filtering Methods for Biomedical Applications

   Author : M. Khalid Khan Niazi; Ewert Bengtsson; Ingela Nyström; Lucas J. van Vliet; Uppsala universitet; []
   Keywords : TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Digital image analysis; Image filtering; Intensity inhomogeneity correction; Empirical mode decomposition; Particle Swarm optimization; Image registration; Computerized Image Processing; Datoriserad bildbehandling;

   Abstract : Filtering is a key step in digital image processing and analysis. It is mainly used for amplification or attenuation of some frequencies depending on the nature of the application. Filtering can either be performed in the spatial domain or in a transformed domain. READ MORE

7. 5. Improving Multi-Atlas Segmentation Methods for Medical Images

   Author : Jennifer Alvén; Chalmers tekniska högskola; []
   Keywords : NATURVETENSKAP; NATURAL SCIENCES; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Supervised learning; semantic segmentation; multi-atlas segmentation; conditional random fields; label fusion; feature-based registration; image registration; random decision forests; convolutional neural networks; medical image segmentation;

   Abstract : Semantic segmentation of organs or tissues, i.e. delineating anatomically or physiologically meaningful boundaries, is an essential task in medical image analysis. One particular class of automatic segmentation algorithms has proved to excel at a diverse set of medical applications, namely multi-atlas segmentation. READ MORE