648 research outputs found
An Automatic Level Set Based Liver Segmentation from MRI Data Sets
A fast and accurate liver segmentation method is a challenging work in medical image analysis area. Liver segmentation is an important process for computer-assisted diagnosis, pre-evaluation of liver transplantation and therapy planning of liver tumors. There are several advantages of magnetic resonance imaging such as free form ionizing radiation and good contrast visualization of soft tissue. Also, innovations in recent technology and image acquisition techniques have made magnetic resonance imaging a major tool in modern medicine. However, the use of magnetic resonance images for liver segmentation has been slow when we compare applications with the central nervous systems and musculoskeletal. The reasons are irregular shape, size and position of the liver, contrast agent effects and similarities of the gray values of neighbor organs. Therefore, in this study, we present a fully automatic liver segmentation method by using an approximation of the level set based contour evolution from T2 weighted magnetic resonance data sets. The method avoids solving partial differential equations and applies only integer operations with a two-cycle segmentation algorithm. The efficiency of the proposed approach is achieved by applying the algorithm to all slices with a constant number of iteration and performing the contour evolution without any user defined initial contour. The obtained results are evaluated with four different similarity measures and they show that the automatic segmentation approach gives successful results
Visual Quality Enhancement in Optoacoustic Tomography using Active Contour Segmentation Priors
Segmentation of biomedical images is essential for studying and
characterizing anatomical structures, detection and evaluation of pathological
tissues. Segmentation has been further shown to enhance the reconstruction
performance in many tomographic imaging modalities by accounting for
heterogeneities of the excitation field and tissue properties in the imaged
region. This is particularly relevant in optoacoustic tomography, where
discontinuities in the optical and acoustic tissue properties, if not properly
accounted for, may result in deterioration of the imaging performance.
Efficient segmentation of optoacoustic images is often hampered by the
relatively low intrinsic contrast of large anatomical structures, which is
further impaired by the limited angular coverage of some commonly employed
tomographic imaging configurations. Herein, we analyze the performance of
active contour models for boundary segmentation in cross-sectional optoacoustic
tomography. The segmented mask is employed to construct a two compartment model
for the acoustic and optical parameters of the imaged tissues, which is
subsequently used to improve accuracy of the image reconstruction routines. The
performance of the suggested segmentation and modeling approach are showcased
in tissue-mimicking phantoms and small animal imaging experiments.Comment: Accepted for publication in IEEE Transactions on Medical Imagin
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
Coupled non-parametric shape and moment-based inter-shape pose priors for multiple basal ganglia structure segmentation
This paper presents a new active contour-based, statistical method for simultaneous volumetric segmentation of multiple subcortical structures in the brain. In biological tissues, such as the human brain, neighboring structures exhibit co-dependencies which can aid in segmentation, if properly analyzed and modeled. Motivated by this observation, we formulate the segmentation problem as a maximum a posteriori estimation problem, in which we incorporate statistical prior models on the shapes and inter-shape (relative) poses of the structures of interest. This provides a principled mechanism to bring high level information about the shapes and the relationships of anatomical structures into the segmentation problem. For learning the prior densities we use a nonparametric multivariate kernel density estimation framework. We combine these priors with data in a variational framework and develop an active contour-based iterative segmentation algorithm.
We test our method on the problem of volumetric segmentation of basal ganglia structures in magnetic resonance (MR) images.
We present a set of 2D and 3D experiments as well as a quantitative performance analysis. In addition, we perform a comparison to several existent segmentation methods and demonstrate the improvements provided by our approach in terms of segmentation accuracy
Automatic Active Contour Modelling and Its Potential Application for Non-Destructive Testing
Active contouring techniques are very useful in medical imaging, digital mapping, non-destructive ultrasonic evaluation, etc. Therefore, we try to explore and investigate the advanced automatic active contouring methods, which can benefit the aforementioned applications. In this thesis work, we study the automatic active contour models adopted for the object characterization, whose extensions could include the potential defect analysis in the ultrasonic non-destructive testing. The active contouring scheme (also called a snake) or an energy minimizing spline, is an algorithm which is very sensitive to the manually marked initial points, and thereby requires an expertly operation. Therefore, we make new research endeavors to handle this major problem and design a new expert-free snake technique, which can lead to the completely automatic contouring technology for the future applications. Even though this initialization problem has been addressed in the literature for quite a while, to the best of our knowledge, there exists no satisfactory solution so far. In this thesis, we propose a novel initialization algorithm for the automatic snake technique, which can possess a faster convergence than other existing automatic contouring methods and also avoid the human operational error incurred in the conventional snake schemes
Computational processing and analysis of ear images
Tese de mestrado. Engenharia Biomédica. Faculdade de Engenharia. Universidade do Porto. 201
PICS in Pics: Physics Informed Contour Selection for Rapid Image Segmentation
Effective training of deep image segmentation models is challenging due to
the need for abundant, high-quality annotations. Generating annotations is
laborious and time-consuming for human experts, especially in medical image
segmentation. To facilitate image annotation, we introduce Physics Informed
Contour Selection (PICS) - an interpretable, physics-informed algorithm for
rapid image segmentation without relying on labeled data. PICS draws
inspiration from physics-informed neural networks (PINNs) and an active contour
model called snake. It is fast and computationally lightweight because it
employs cubic splines instead of a deep neural network as a basis function. Its
training parameters are physically interpretable because they directly
represent control knots of the segmentation curve. Traditional snakes involve
minimization of the edge-based loss functionals by deriving the Euler-Lagrange
equation followed by its numerical solution. However, PICS directly minimizes
the loss functional, bypassing the Euler Lagrange equations. It is the first
snake variant to minimize a region-based loss function instead of traditional
edge-based loss functions. PICS uniquely models the three-dimensional (3D)
segmentation process with an unsteady partial differential equation (PDE),
which allows accelerated segmentation via transfer learning. To demonstrate its
effectiveness, we apply PICS for 3D segmentation of the left ventricle on a
publicly available cardiac dataset. While doing so, we also introduce a new
convexity-preserving loss term that encodes the shape information of the left
ventricle to enhance PICS's segmentation quality. Overall, PICS presents
several novelties in network architecture, transfer learning, and
physics-inspired losses for image segmentation, thereby showing promising
outcomes and potential for further refinement
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