707 research outputs found
Construction of boundary element models in bioelectromagnetism
Multisensor electro- and magnetoencephalographic (EEG and MEG) as well as electro- and magnetocardiographic (ECG and MCG) recordings have been proved useful in noninvasively extracting information on bioelectric excitation. The anatomy of the patient needs to be taken into account, when excitation sites are localized by solving the inverse problem. In this work, a methodology has been developed to construct patient specific boundary element models for bioelectromagnetic inverse problems from magnetic resonance (MR) data volumes as well as from two orthogonal X-ray projections. The process consists of three main steps: reconstruction of 3-D geometry, triangulation of reconstructed geometry, and registration of the model with a bioelectromagnetic measurement system. The 3-D geometry is reconstructed from MR data by matching a 3-D deformable boundary element template to images. The deformation is accomplished as an energy minimization process consisting of image and model based terms. The robustness of the matching is improved by multi-resolution and global-to-local approaches as well as using oriented distance maps. A boundary element template is also used when 3-D geometry is reconstructed from X-ray projections. The deformation is first accomplished in 2-D for the contours of simulated, built from the template, and real X-ray projections. The produced 2-D vector field is back-projected and interpolated on the 3-D template surface. A marching cube triangulation is computed for the reconstructed 3-D geometry. Thereafter, a non-iterative mesh-simplification method is applied. The method is based on the Voronoi-Delaunay duality on a 3-D surface with discrete distance measures. Finally, the triangulated surfaces are registered with a bioelectromagnetic measurement utilizing markers. More than fifty boundary element models have been successfully constructed from MR images using the methods developed in this work. A simulation demonstrated the feasibility of X-ray reconstruction; some practical problems of X-ray imaging need to be solved to begin tests with real data.reviewe
Regmentation: A New View of Image Segmentation and Registration
Image segmentation and registration have been the two major areas of research in the medical imaging community for decades and still are. In the context of radiation oncology, segmentation and registration methods are widely used for target structure definition such as prostate or head and neck lymph node areas. In the past two years, 45% of all articles published in the most important medical imaging journals and conferences have presented either segmentation or registration methods. In the literature, both categories are treated rather separately even though they have much in common. Registration techniques are used to solve segmentation tasks (e.g. atlas based methods) and vice versa (e.g. segmentation of structures used in a landmark based registration). This article reviews the literature on image segmentation methods by introducing a novel taxonomy based on the amount of shape knowledge being incorporated in the segmentation process. Based on that, we argue that all global shape prior segmentation methods are identical to image registration methods and that such methods thus cannot be characterized as either image segmentation or registration methods. Therefore we propose a new class of methods that are able solve both segmentation and registration tasks. We call it regmentation. Quantified on a survey of the current state of the art medical imaging literature, it turns out that 25% of the methods are pure registration methods, 46% are pure segmentation methods and 29% are regmentation methods. The new view on image segmentation and registration provides a consistent taxonomy in this context and emphasizes the importance of regmentation in current medical image processing research and radiation oncology image-guided applications
Segmentation and Deformable Modelling Techniques for a Virtual Reality Surgical Simulator in Hepatic Oncology
Liver surgical resection is one of the most frequently used curative therapies. However,
resectability is problematic. There is a need for a computer-assisted surgical planning and
simulation system which can accurately and efficiently simulate the liver, vessels and
tumours in actual patients. The present project describes the development of these core
segmentation and deformable modelling techniques.
For precise detection of irregularly shaped areas with indistinct boundaries, the
segmentation incorporated active contours - gradient vector flow (GVF) snakes and level sets.
To improve efficiency, a chessboard distance transform was used to replace part of the GVF
effort. To automatically initialize the liver volume detection process, a rotating template was
introduced to locate the starting slice. For shape maintenance during the segmentation
process, a simplified object shape learning step was introduced to avoid occasional
significant errors. Skeletonization with fuzzy connectedness was used for vessel
segmentation.
To achieve real-time interactivity, the deformation regime of this system was based
on a single-organ mass-spring system (MSS), which introduced an on-the-fly local mesh
refinement to raise the deformation accuracy and the mesh control quality. This method was
now extended to a multiple soft-tissue constraint system, by supplementing it with an
adaptive constraint mesh generation. A mesh quality measure was tailored based on a wide
comparison of classic measures. Adjustable feature and parameter settings were thus
provided, to make tissues of interest distinct from adjacent structures, keeping the mesh
suitable for on-line topological transformation and deformation.
More than 20 actual patient CT and 2 magnetic resonance imaging (MRI) liver
datasets were tested to evaluate the performance of the segmentation method. Instrument
manipulations of probing, grasping, and simple cutting were successfully simulated on
deformable constraint liver tissue models. This project was implemented in conjunction with
the Division of Surgery, Hammersmith Hospital, London; the preliminary reality effect was
judged satisfactory by the consultant hepatic surgeon
Statistical Shape Modelling and Segmentation of the Respiratory Airway
The human respiratory airway consists of the upper (nasal cavity, pharynx) and the lower (trachea, bronchi) respiratory tracts. Accurate segmentation of these two airway tracts can lead to better diagnosis and interpretation of airway-specific diseases, and lead to improvement in the localization of abnormal metabolic or pathological sites found within and/or surrounding the respiratory regions. Due to the complexity and the variability displayed in the anatomical structure of the upper respiratory airway along with the challenges in distinguishing the nasal cavity from non-respiratory regions such as the paranasal sinuses, it is difficult for existing algorithms to accurately segment the upper airway without manual intervention. This thesis presents an implicit non-parametric framework for constructing a statistical shape model (SSM) of the upper and lower respiratory tract, capable of distinct shape generation and be adapted for segmentation. An SSM of the nasal cavity was successfully constructed using 50 nasal CT scans. The performance of the SSM was evaluated for compactness, specificity and generality. An averaged distance error of 1.47 mm was measured for the generality assessment. The constructed SSM was further adapted with a modified locally constrained random walk algorithm to segment the nasal cavity. The proposed algorithm was evaluated on 30 CT images and outperformed comparative state-of-the-art and conventional algorithms. For the lower airway, a separate algorithm was proposed to automatically segment the trachea and bronchi, and was designed to tolerate the image characteristics inherent in low-contrast CT images. The algorithm was evaluated on 20 clinical low-contrast CT from PET-CT patient studies and demonstrated better performance (87.1±2.8 DSC and distance error of 0.37±0.08 mm) in segmentation results against comparative state-of-the-art algorithms
The Probabilistic Active Shape Model: From Model Construction to Flexible Medical Image Segmentation
Automatic processing of three-dimensional image data acquired with computed tomography or magnetic resonance imaging plays an increasingly important role in medicine. For example, the automatic
segmentation of anatomical structures in tomographic images allows to generate three-dimensional visualizations of a patient’s anatomy and thereby supports surgeons during planning of various kinds of
surgeries.
Because organs in medical images often exhibit a low contrast to adjacent structures, and because the image quality may be hampered by noise or other image acquisition artifacts, the development of segmentation algorithms that are both robust and accurate is very challenging. In order to increase the robustness, the use of model-based algorithms is mandatory, as for example algorithms that incorporate prior knowledge about an organ’s shape into the segmentation process. Recent research has proven that Statistical Shape Models are especially appropriate for robust medical image segmentation. In these models, the typical shape of an organ is learned from a set of training examples. However, Statistical Shape Models have two major disadvantages: The construction of the models is relatively difficult, and the models are often used too restrictively, such that the resulting segmentation does not delineate the organ exactly.
This thesis addresses both problems: The first part of the thesis introduces new methods for establishing correspondence between training shapes, which is a necessary prerequisite for shape model learning. The developed methods include consistent parameterization algorithms for organs with spherical and genus 1 topology, as well as a nonrigid mesh registration algorithm for shapes with arbitrary topology. The second part of the thesis presents a new shape model-based segmentation algorithm that allows for an accurate delineation of organs. In contrast to existing approaches, it is possible to integrate not only linear shape models into the algorithm, but also nonlinear shape models, which allow for a more specific description of an organ’s shape variation.
The proposed segmentation algorithm is evaluated in three applications to medical image data: Liver and vertebra segmentation in contrast-enhanced computed tomography scans, and prostate segmentation in magnetic resonance images
Segmentation, tracking, and kinematics of lung parenchyma and lung tumors from 4D CT with application to radiation treatment planning.
This thesis is concerned with development of techniques for efficient computerized analysis of 4-D CT data. The goal is to have a highly automated approach to segmentation of the lung boundary and lung nodules inside the lung. The determination of exact lung tumor location over space and time by image segmentation is an essential step to track thoracic malignancies. Accurate image segmentation helps clinical experts examine the anatomy and structure and determine the disease progress. Since 4-D CT provides structural and anatomical information during tidal breathing, we use the same data to also measure mechanical properties related to deformation of the lung tissue including Jacobian and strain at high resolutions and as a function of time. Radiation Treatment of patients with lung cancer can benefit from knowledge of these measures of regional ventilation. Graph-cuts techniques have been popular for image segmentation since they are able to treat highly textured data via robust global optimization, avoiding local minima in graph based optimization. The graph-cuts methods have been used to extract globally optimal boundaries from images by s/t cut, with energy function based on model-specific visual cues, and useful topological constraints. The method makes N-dimensional globally optimal segmentation possible with good computational efficiency. Even though the graph-cuts method can extract objects where there is a clear intensity difference, segmentation of organs or tumors pose a challenge. For organ segmentation, many segmentation methods using a shape prior have been proposed. However, in the case of lung tumors, the shape varies from patient to patient, and with location. In this thesis, we use a shape prior for tumors through a training step and PCA analysis based on the Active Shape Model (ASM). The method has been tested on real patient data from the Brown Cancer Center at the University of Louisville. We performed temporal B-spline deformable registration of the 4-D CT data - this yielded 3-D deformation fields between successive respiratory phases from which measures of regional lung function were determined. During the respiratory cycle, the lung volume changes and five different lobes of the lung (two in the left and three in the right lung) show different deformation yielding different strain and Jacobian maps. In this thesis, we determine the regional lung mechanics in the Lagrangian frame of reference through different respiratory phases, for example, Phase10 to 20, Phase10 to 30, Phase10 to 40, and Phase10 to 50. Single photon emission computed tomography (SPECT) lung imaging using radioactive tracers with SPECT ventilation and SPECT perfusion imaging also provides functional information. As part of an IRB-approved study therefore, we registered the max-inhale CT volume to both VSPECT and QSPECT data sets using the Demon\u27s non-rigid registration algorithm in patient subjects. Subsequently, statistical correlation between CT ventilation images (Jacobian and strain values), with both VSPECT and QSPECT was undertaken. Through statistical analysis with the Spearman\u27s rank correlation coefficient, we found that Jacobian values have the highest correlation with both VSPECT and QSPECT
Patch-based segmentation with spatial context for medical image analysis
Accurate segmentations in medical imaging form a crucial role in many applications from pa-
tient diagnosis to population studies. As the amount of data generated from medical images
increases, the ability to perform this task without human intervention becomes ever more de-
sirable. One approach, known broadly as atlas-based segmentation, is to propagate labels from
images which have already been manually labelled by clinical experts. Methods using this ap-
proach have been shown to be e ective in many applications, demonstrating great potential for
automatic labelling of large datasets. However, these methods usually require the use of image
registration and are dependent on the outcome of the registration. Any registrations errors
that occur are also propagated to the segmentation process and are likely to have an adverse
e ect on segmentation accuracy. Recently, patch-based methods have been shown to allow a
relaxation of the required image alignment, whilst achieving similar results. In general, these
methods label each voxel of a target image by comparing the image patch centred on the voxel
with neighbouring patches from an atlas library and assigning the most likely label according
to the closest matches. The main contributions of this thesis focuses around this approach
in providing accurate segmentation results whilst minimising the dependency on registration
quality. In particular, this thesis proposes a novel kNN patch-based segmentation framework,
which utilises both intensity and spatial information, and explore the use of spatial context in
a diverse range of applications. The proposed methods extend the potential for patch-based
segmentation to tolerate registration errors by rede ning the \locality" for patch selection and
comparison, whilst also allowing similar looking patches from di erent anatomical structures
to be di erentiated. The methods are evaluated on a wide variety of image datasets, ranging
from the brain to the knees, demonstrating its potential with results which are competitive to
state-of-the-art techniques.Open Acces
Image analysis for extracapsular hip fracture surgery
PhD ThesisDuring the implant insertion phase of extracapsular hip fracture surgery, a
surgeon visually inspects digital radiographs to infer the best position for
the implant. The inference is made by “eye-balling”. This clearly leaves
room for trial and error which is not ideal for the patient.
This thesis presents an image analysis approach to estimating the ideal positioning
for the implant using a variant of the deformable templates model
known as the Constrained Local Model (CLM). The Model is a synthesis of
shape and local appearance models learned from a set of annotated landmarks
and their corresponding local patches extracted from digital femur
x-rays.
The CLM in this work highlights both Principal Component Analysis (PCA)
and Probabilistic PCA as regularisation components; the PPCA variant being
a novel adaptation of the CLM framework that accounts for landmark
annotation error which the PCA version does not account for. Our CLM
implementation is used to articulate 2 clinical metrics namely:
the Tip-Apex Distance and Parker’s Ratio (routinely used by clinicians to assess
the positioning of the surgical implant during hip fracture surgery)
within the image analysis framework. With our model, we were able to
automatically localise signi cant landmarks on the femur, which were
subsequently used to measure Parker’s Ratio directly from digital radiographs
and determine an optimal placement for the surgical implant in
87% of the instances; thereby, achieving fully automatic measurement of
Parker’s Ratio as opposed to manual measurements currently performed
in the surgical theatre during hip fracture surgery
- …