655 research outputs found
Foetal echocardiographic segmentation
Congenital heart disease affects just under one percentage of all live births [1].
Those defects that manifest themselves as changes to the cardiac chamber volumes
are the motivation for the research presented in this thesis.
Blood volume measurements in vivo require delineation of the cardiac chambers and
manual tracing of foetal cardiac chambers is very time consuming and operator
dependent. This thesis presents a multi region based level set snake deformable
model applied in both 2D and 3D which can automatically adapt to some extent
towards ultrasound noise such as attenuation, speckle and partial occlusion artefacts.
The algorithm presented is named Mumford Shah Sarti Collision Detection (MSSCD).
The level set methods presented in this thesis have an optional shape prior term for
constraining the segmentation by a template registered to the image in the presence
of shadowing and heavy noise.
When applied to real data in the absence of the template the MSSCD algorithm is
initialised from seed primitives placed at the centre of each cardiac chamber. The
voxel statistics inside the chamber is determined before evolution. The MSSCD stops
at open boundaries between two chambers as the two approaching level set fronts
meet. This has significance when determining volumes for all cardiac compartments
since cardiac indices assume that each chamber is treated in isolation. Comparison
of the segmentation results from the implemented snakes including a previous level
set method in the foetal cardiac literature show that in both 2D and 3D on both real
and synthetic data, the MSSCD formulation is better suited to these types of data.
All the algorithms tested in this thesis are within 2mm error to manually traced
segmentation of the foetal cardiac datasets. This corresponds to less than 10% of
the length of a foetal heart. In addition to comparison with manual tracings all the
amorphous deformable model segmentations in this thesis are validated using a
physical phantom. The volume estimation of the phantom by the MSSCD
segmentation is to within 13% of the physically determined volume
Tools for developing continuous-flow micro-mixer : numerical simulation of transitional flow in micro geometries and a quantitative technique for extracting dynamic information from micro-bubble images
Recent advance in the microfluidics including its fabrication technologies has
led to many novel applications in micro-scale flows. Among them is the
continuous-flow micromixer that utilizes the advantages associated with
turbulent flows for rapid mixing, achieving the detection of fast kinetic
reaction as short as tens of microseconds. However, for developing a high
performance continuous-flow micromixer there are certain fundamental
issues need to be solved. One of them is an universal simulation approach
capable of calculating the flow field across entire passage for entire regime
from very low Reynolds number laminar flow through transition to fully
turbulent flow. Though the direct numerical simulation is potentially possible
solution but its extremely high computing time stops itself from practical
applications. The second major issue is the inevitable occurrence of
cavitation bubbles in this rapid flow apparatus. This phenomenon has
opposite effects: (a) deteriorating performance and damaging the micromixer;
(b) playing a catalyst role in enhancing mixing. A fully understanding of
these micro bubbles will provide a sound theoretical base for guiding the design of micromixer in order to explore the advantage to maximum while
minimizing its disadvantages. Therefore, the objectives of this PhD
programme is to study the tools that will effectively advance our fundamental
understandings on these key issues while in short term fulfil the requires from
the joint experimental PhD programme held in the life science faculty for
designing a prototype experimental device. During this PhD study, an
existing numerical approach suitable for predicting the possibly entire flow
regime including the turbulence transition is proposed for simulating the
microscale flows in the microchannel and micromixer. The simulation results
are validated against the transitional micro-channel experiments and this
numerical method is then further applied for the micromixer simulation. This
provides the researcher a realistic and feasible CFD tool to establish
guidelines for designing high-efficiency and cost-effective micromixers by
utilizing various possible measures which may cause very different flows
simultaneously in micromixer. In order to study microscale cavitation
bubbles and their effects on micromixers, an innovative experimental setup is
purposely designed and constructed that can generate laser-induced
micro-bubbles at desired position and size for testing. Experiments withvarious micro-scale bubbles have been performed successfully by using an
ultra high-speed camera up to 1 million frame rate per second. A novel
technique for tracking the contours of micro-scale cavitation bubble
dynamically has been developed by using active contour method. By using
this technique, for the first time, various geometric and dynamic data of
cavitation bubble have been obtained to quantitatively analyze the global
behaviours of bubbles thoroughly. This powerful tool will greatly benefit the
study of bubble dynamics and similar demands in other fields for fast and
accurate image treatments as well
Smart Cage Active Contours and their application to brain image segmentation
In this work we present a new segmentation method named Smart Cage
Active Contours (SCAC) that combines a parametrized active contour
framework named Cage Active Contours (CAC), based on a ne trans-
formations, with Active Shape Models (ASM). Our method e ectively
restricts the shapes the evolving contours can take without the need of
the training images to be manually landmarked. We apply our method to
segment the caudate nuclei subcortical structure of a set of 40 subjects in
magnetic resonance brain images, with promising results
Segmentation in Echocardiographic Sequences Using Shape-based Snake Model
A method for segmentation of cardiac structures especially for mitral valve in echocardiographic sequences is presented. The method is motivated by the observation that the structures of neighboring frames have consistent locations and shapes that aid in segmentation. To cooperate with the constraining information provided by the neighboring frames, we combine the template matching with the conventional snake model. It means that the model not only is driven by conventional internal and external forces, but also combines an additional constraint, the matching degree to measure the similarity between the neighboring prior shape and the derived contour. Furthermore, in order to automatically or semi-automatically segment the sequent images without manually drawing the initial contours in each image, generalized Hough transformation (GHT) is used to roughly estimate the initial contour by transforming the neighboring prior shape. Based on the experiments on forty sequences, the method is particularly useful in case of the large frame-to-frame displacement of structure such as mitral valve. As a result, the active contour can easily detect the desirable boundaries in ultrasound images and has a high penetrability through the interference of various undesirables, such as the speckle, the tissue-related textures and the artifacts
Multi-scale active shape description in medical imaging
Shape description in medical imaging has become an increasingly important research field in recent years. Fast and high-resolution image acquisition methods like Magnetic Resonance (MR) imaging produce very detailed cross-sectional images of the human body - shape description is then a post-processing operation which abstracts quantitative descriptions of anatomically relevant object shapes. This task is usually performed by clinicians and other experts by first segmenting the shapes of interest, and then making volumetric and other quantitative measurements. High demand on expert time and inter- and intra-observer variability impose a clinical need of automating this process. Furthermore, recent studies in clinical neurology on the correspondence between disease status and degree of shape deformations necessitate the use of more sophisticated, higher-level shape description techniques. In this work a new hierarchical tool for shape description has been developed, combining two recently developed and powerful techniques in image processing: differential invariants in scale-space, and active contour models. This tool enables quantitative and qualitative shape studies at multiple levels of image detail, exploring the extra image scale degree of freedom. Using scale-space continuity, the global object shape can be detected at a coarse level of image detail, and finer shape characteristics can be found at higher levels of detail or scales. New methods for active shape evolution and focusing have been developed for the extraction of shapes at a large set of scales using an active contour model whose energy function is regularized with respect to scale and geometric differential image invariants. The resulting set of shapes is formulated as a multiscale shape stack which is analysed and described for each scale level with a large set of shape descriptors to obtain and analyse shape changes across scales. This shape stack leads naturally to several questions in regard to variable sampling and appropriate levels of detail to investigate an image. The relationship between active contour sampling precision and scale-space is addressed. After a thorough review of modem shape description, multi-scale image processing and active contour model techniques, the novel framework for multi-scale active shape description is presented and tested on synthetic images and medical images. An interesting result is the recovery of the fractal dimension of a known fractal boundary using this framework. Medical applications addressed are grey-matter deformations occurring for patients with epilepsy, spinal cord atrophy for patients with Multiple Sclerosis, and cortical impairment for neonates. Extensions to non-linear scale-spaces, comparisons to binary curve and curvature evolution schemes as well as other hierarchical shape descriptors are discussed
Multi-Surface Simplex Spine Segmentation for Spine Surgery Simulation and Planning
This research proposes to develop a knowledge-based multi-surface simplex deformable model for segmentation of healthy as well as pathological lumbar spine data. It aims to provide a more accurate and robust segmentation scheme for identification of intervertebral disc pathologies to assist with spine surgery planning. A robust technique that combines multi-surface and shape statistics-aware variants of the deformable simplex model is presented. Statistical shape variation within the dataset has been captured by application of principal component analysis and incorporated during the segmentation process to refine results. In the case where shape statistics hinder detection of the pathological region, user-assistance is allowed to disable the prior shape influence during deformation. Results have been validated against user-assisted expert segmentation
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