5 research outputs found
Bayesian Optimization for Image Segmentation, Texture Flow Estimation and Image Deblurring
Ph.DDOCTOR OF PHILOSOPH
Image Based Biomarkers from Magnetic Resonance Modalities: Blending Multiple Modalities, Dimensions and Scales.
The successful analysis and processing of medical
imaging data is a multidisciplinary work that requires the
application and combination of knowledge from diverse fields,
such as medical engineering, medicine, computer science and
pattern classification. Imaging biomarkers are biologic features
detectable by imaging modalities and their use offer the prospect
of more efficient clinical studies and improvement in both
diagnosis and therapy assessment. The use of Dynamic Contrast
Enhanced Magnetic Resonance Imaging (DCE-MRI) and its
application to the diagnosis and therapy has been extensively
validated, nevertheless the issue of an appropriate or optimal
processing of data that helps to extract relevant biomarkers
to highlight the difference between heterogeneous tissue still
remains. Together with DCE-MRI, the data extracted from
Diffusion MRI (DWI-MR and DTI-MR) represents a promising
and complementary tool. This project initially proposes the
exploration of diverse techniques and methodologies for the
characterization of tissue, following an analysis and classification
of voxel-level time-intensity curves from DCE-MRI data mainly
through the exploration of dissimilarity based representations
and models. We will explore metrics and representations to
correlate the multidimensional data acquired through diverse
imaging modalities, a work which starts with the appropriate
elastic registration methodology between DCE-MRI and DWI-
MR on the breast and its corresponding validation.
It has been shown that the combination of multi-modal MRI
images improve the discrimination of diseased tissue. However the fusion
of dissimilar imaging data for classification and segmentation purposes is
not a trivial task, there is an inherent difference in information domains,
dimensionality and scales. This work also proposes a multi-view consensus
clustering methodology for the integration of multi-modal MR images
into a unified segmentation of tumoral lesions for heterogeneity assessment. Using a variety of metrics and distance functions this multi-view
imaging approach calculates multiple vectorial dissimilarity-spaces for
each one of the MRI modalities and makes use of the concepts behind
cluster ensembles to combine a set of base unsupervised segmentations
into an unified partition of the voxel-based data. The methodology is
specially designed for combining DCE-MRI and DTI-MR, for which a
manifold learning step is implemented in order to account for the geometric constrains of the high dimensional diffusion information.The successful analysis and processing of medical
imaging data is a multidisciplinary work that requires the
application and combination of knowledge from diverse fields,
such as medical engineering, medicine, computer science and
pattern classification. Imaging biomarkers are biologic features
detectable by imaging modalities and their use offer the prospect
of more efficient clinical studies and improvement in both
diagnosis and therapy assessment. The use of Dynamic Contrast
Enhanced Magnetic Resonance Imaging (DCE-MRI) and its
application to the diagnosis and therapy has been extensively
validated, nevertheless the issue of an appropriate or optimal
processing of data that helps to extract relevant biomarkers
to highlight the difference between heterogeneous tissue still
remains. Together with DCE-MRI, the data extracted from
Diffusion MRI (DWI-MR and DTI-MR) represents a promising
and complementary tool. This project initially proposes the
exploration of diverse techniques and methodologies for the
characterization of tissue, following an analysis and classification
of voxel-level time-intensity curves from DCE-MRI data mainly
through the exploration of dissimilarity based representations
and models. We will explore metrics and representations to
correlate the multidimensional data acquired through diverse
imaging modalities, a work which starts with the appropriate
elastic registration methodology between DCE-MRI and DWI-
MR on the breast and its corresponding validation.
It has been shown that the combination of multi-modal MRI
images improve the discrimination of diseased tissue. However the fusion
of dissimilar imaging data for classification and segmentation purposes is
not a trivial task, there is an inherent difference in information domains,
dimensionality and scales. This work also proposes a multi-view consensus
clustering methodology for the integration of multi-modal MR images
into a unified segmentation of tumoral lesions for heterogeneity assessment. Using a variety of metrics and distance functions this multi-view
imaging approach calculates multiple vectorial dissimilarity-spaces for
each one of the MRI modalities and makes use of the concepts behind
cluster ensembles to combine a set of base unsupervised segmentations
into an unified partition of the voxel-based data. The methodology is
specially designed for combining DCE-MRI and DTI-MR, for which a
manifold learning step is implemented in order to account for the geometric constrains of the high dimensional diffusion information
A video summarisation system for post-production
Post-production facilities deal with large amounts of digital video, which presents difficulties when tracking, managing and searching this material. Recent research work in image and video analysis promises to offer help in these tasks, but there is a gap between what these systems can provide and what users actually need. In particular the popular research models for indexing and retrieving visual data do not fit well with how users actually work. In this thesis we explore how image and video analysis can be applied to an online video collection to assist users in reviewing and searching for material faster, rather than purporting to do it for them.
We introduce a framework for automatically generating static 2-dimen- sional storyboards from video sequences. The storyboard consists of a series of frames, one for each shot in the sequence, showing the principal objects and motions of the shot. The storyboards are rendered as vector images in a familiar comic book style, allowing them to be quickly viewed and understood. The process consists of three distinct steps: shot-change detection, object segmentation, and presentation.
The nature of the video material encountered in a post-production fa- cility is quite different from other material such as television programmes. Video sequences such as commercials and music videos are highly dy- namic with very short shots, rapid transitions and ambiguous edits. Video is often heavily manipulated, causing difficulties for many video processing techniques.
We study the performance of a variety of published shot-change de- tection algorithms on the type of highly dynamic video typically encoun- tered in post-production work. Finding their performance disappointing, we develop a novel algorithm for detecting cuts and fades that operates directly on Motion-JPEG compressed video, exploiting the DCT coeffi- cients to save computation. The algorithm shows superior performance on highly dynamic material while performing comparably to previous algorithms on other material
Advanced Image Acquisition, Processing Techniques and Applications
"Advanced Image Acquisition, Processing Techniques and Applications" is the first book of a series that provides image processing principles and practical software implementation on a broad range of applications. The book integrates material from leading researchers on Applied Digital Image Acquisition and Processing. An important feature of the book is its emphasis on software tools and scientific computing in order to enhance results and arrive at problem solution
Handbook of Mathematical Geosciences
This Open Access handbook published at the IAMG's 50th anniversary, presents a compilation of invited path-breaking research contributions by award-winning geoscientists who have been instrumental in shaping the IAMG. It contains 45 chapters that are categorized broadly into five parts (i) theory, (ii) general applications, (iii) exploration and resource estimation, (iv) reviews, and (v) reminiscences covering related topics like mathematical geosciences, mathematical morphology, geostatistics, fractals and multifractals, spatial statistics, multipoint geostatistics, compositional data analysis, informatics, geocomputation, numerical methods, and chaos theory in the geosciences