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Shape-driven segmentation of the arterial wall in intravascular ultrasound images
Segmentation of arterial wall boundaries from intravascular images is an important problem for many applications in the study of plaque characteristics, mechanical properties of the arterial wall, its 3D reconstruction,
and its measurements such as lumen size, lumen radius, and wall radius. We present a shape-driven approach to segmentation of the arterial wall from intravascular ultrasound images in the rectangular domain. In a properly built
shape space using training data, we constrain the lumen and media-adventitia contours to a smooth, closed geometry, which increases the segmentation quality without any tradeoff with a regularizer term. In addition to a shape prior,
we utilize an intensity prior through a non-parametric probability density based image energy, with global image measurements rather than pointwise measurements used in previous methods. Furthermore, a detection step is included to address the challenges introduced to the segmentation process by side branches and calcifications. All these features greatly enhance our segmentation method. The tests of our algorithm on a large dataset demonstrate the effectiveness of our approach
Segmentation of the left ventricle of the heart in 3-D+t MRI data using an optimized nonrigid temporal model
Modern medical imaging modalities provide large amounts of information in both the spatial and temporal domains and the incorporation of this information in a coherent algorithmic framework is a significant challenge. In this paper, we present a novel and intuitive approach to combine 3-D spatial and temporal (3-D + time) magnetic resonance imaging (MRI) data in an integrated segmentation algorithm to extract the myocardium of the left ventricle. A novel level-set segmentation process is developed that simultaneously delineates and tracks the boundaries of the left ventricle muscle. By encoding prior knowledge about cardiac temporal evolution in a parametric framework, an expectation-maximization algorithm optimally tracks the myocardial deformation over the cardiac cycle. The expectation step deforms the level-set function while the maximization step updates the prior temporal model parameters to perform the segmentation in a nonrigid sense
Analysis of Amoeba Active Contours
Subject of this paper is the theoretical analysis of structure-adaptive
median filter algorithms that approximate curvature-based PDEs for image
filtering and segmentation. These so-called morphological amoeba filters are
based on a concept introduced by Lerallut et al. They achieve similar results
as the well-known geodesic active contour and self-snakes PDEs. In the present
work, the PDE approximated by amoeba active contours is derived for a general
geometric situation and general amoeba metric. This PDE is structurally similar
but not identical to the geodesic active contour equation. It reproduces the
previous PDE approximation results for amoeba median filters as special cases.
Furthermore, modifications of the basic amoeba active contour algorithm are
analysed that are related to the morphological force terms frequently used with
geodesic active contours. Experiments demonstrate the basic behaviour of amoeba
active contours and its similarity to geodesic active contours.Comment: Revised version with several improvements for clarity, slightly
extended experiments and discussion. Accepted for publication in Journal of
Mathematical Imaging and Visio
Achieving the Way for Automated Segmentation of Nuclei in Cancer Tissue Images through Morphology-Based Approach: a Quantitative Evaluation
In this paper we address the problem of nuclear segmentation in cancer tissue images, that is critical for specific protein activity quantification and for cancer diagnosis and therapy. We present a fully automated morphology-based technique able to perform accurate nuclear segmentations in images with heterogeneous staining and multiple tissue layers and we compare it with an alternate semi-automated method based on a well established segmentation approach, namely active contours. We discuss active contours’ limitations in the segmentation of immunohistochemical images and we demonstrate and motivate through extensive experiments the better accuracy of our fully automated approach compared to various active contours implementations
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
Image and Volume Segmentation by Water Flow
A general framework for image segmentation is presented in this paper, based on the paradigm of water flow. The major water flow attributes like water pressure, surface tension and capillary force are defined in the context of force field generation and make the model adaptable to topological and geometrical changes. A flow-stopping image functional combining edge- and region-based forces is introduced to produce capability for both range and accuracy. The method is assessed qualitatively and quantitatively on synthetic and natural images. It is shown that the new approach can segment objects with complex shapes or weak-contrasted boundaries, and has good immunity to noise. The operator is also extended to 3-D, and is successfully applied to medical volume segmentation
On the segmentation of astronomical images via level-set methods
Astronomical images are of crucial importance for astronomers since they
contain a lot of information about celestial bodies that can not be directly
accessible. Most of the information available for the analysis of these objects
starts with sky explorations via telescopes and satellites. Unfortunately, the
quality of astronomical images is usually very low with respect to other real
images and this is due to technical and physical features related to their
acquisition process. This increases the percentage of noise and makes more
difficult to use directly standard segmentation methods on the original image.
In this work we will describe how to process astronomical images in two steps:
in the first step we improve the image quality by a rescaling of light
intensity whereas in the second step we apply level-set methods to identify the
objects. Several experiments will show the effectiveness of this procedure and
the results obtained via various discretization techniques for level-set
equations.Comment: 24 pages, 59 figures, paper submitte
Automated detection of extended sources in radio maps: progress from the SCORPIO survey
Automated source extraction and parameterization represents a crucial
challenge for the next-generation radio interferometer surveys, such as those
performed with the Square Kilometre Array (SKA) and its precursors. In this
paper we present a new algorithm, dubbed CAESAR (Compact And Extended Source
Automated Recognition), to detect and parametrize extended sources in radio
interferometric maps. It is based on a pre-filtering stage, allowing image
denoising, compact source suppression and enhancement of diffuse emission,
followed by an adaptive superpixel clustering stage for final source
segmentation. A parameterization stage provides source flux information and a
wide range of morphology estimators for post-processing analysis. We developed
CAESAR in a modular software library, including also different methods for
local background estimation and image filtering, along with alternative
algorithms for both compact and diffuse source extraction. The method was
applied to real radio continuum data collected at the Australian Telescope
Compact Array (ATCA) within the SCORPIO project, a pathfinder of the ASKAP-EMU
survey. The source reconstruction capabilities were studied over different test
fields in the presence of compact sources, imaging artefacts and diffuse
emission from the Galactic plane and compared with existing algorithms. When
compared to a human-driven analysis, the designed algorithm was found capable
of detecting known target sources and regions of diffuse emission,
outperforming alternative approaches over the considered fields.Comment: 15 pages, 9 figure
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