5,671 research outputs found
Segmentation of Myocardial Boundaries in Tagged Cardiac MRI Using Active Contours: A Gradient-Based Approach Integrating Texture Analysis
The noninvasive assessment of cardiac function is of first importance for the diagnosis of cardiovascular diseases. Among all medical scanners only a few enables radiologists to evaluate the local cardiac motion. Tagged cardiac MRI is one of them. This protocol generates on Short-Axis (SA) sequences a dark grid which is deformed in accordance with the cardiac motion. Tracking the grid allows specialists a local estimation of cardiac geometrical parameters within myocardium. The work described in this paper aims to automate the myocardial contours detection in order to optimize the detection and the tracking of the grid of tags within myocardium. The method we have developed for endocardial and epicardial contours detection is based on the use of texture analysis and active contours models. Texture analysis allows us to define energy maps more efficient than those usually used in active contours methods where attractor is often based on gradient and which were useless in our case of study, for quality of tagged cardiac MRI is very poor
A survey on 2d object tracking in digital video
This paper presents object tracking methods in video.Different algorithms based on rigid, non rigid and articulated object tracking are studied. The goal of this article is to review the state-of-the-art tracking methods, classify them
into different categories, and identify new trends.It is often the case that tracking objects in consecutive frames is supported by a prediction scheme. Based on information extracted from previous frames and any high level information that can be obtained, the state (location) of the
object is predicted.An excellent framework for prediction is kalman filter, which additionally estimates prediction error.In complex scenes, instead of single hypothesis, multiple hypotheses using Particle filter can be used.Different
techniques are given for different types of constraints in video
Real-time people tracking in a camera network
Visual tracking is a fundamental key to the recognition and analysis of human behaviour.
In this thesis we present an approach to track several subjects using multiple
cameras in real time. The tracking framework employs a numerical Bayesian estimator,
also known as a particle lter, which has been developed for parallel implementation on
a Graphics Processing Unit (GPU). In order to integrate multiple cameras into a single
tracking unit we represent the human body by a parametric ellipsoid in a 3D world.
The elliptical boundary can be projected rapidly, several hundred times per subject per
frame, onto any image for comparison with the image data within a likelihood model.
Adding variables to encode visibility and persistence into the state vector, we tackle the
problems of distraction and short-period occlusion. However, subjects may also disappear
for longer periods due to blind spots between cameras elds of view. To recognise
a desired subject after such a long-period, we add coloured texture to the ellipsoid surface,
which is learnt and retained during the tracking process. This texture signature
improves the recall rate from 60% to 70-80% when compared to state only data association.
Compared to a standard Central Processing Unit (CPU) implementation, there
is a signi cant speed-up ratio
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Statistical Region Based Segmentation of Ultrasound Images
Segmentation of ultrasound images is a challenging problem due to speckle, which
corrupts the image and can result in weak or missing image boundaries, poor signal to
noise ratio, and diminished contrast resolution. Speckle is a random interference pattern
that is characterized by an asymmetric distribution as well as significant spatial correla-
tion. These attributes of speckle are challenging to model in a segmentation approach, so
many previous ultrasound segmentation methods simplify the problem by assuming that
the speckle is white and/or Gaussian distributed. Unlike these methods, in this paper
we present an ultrasound-specific segmentation approach that addresses both the spatial
correlation of the data as well as its intensity distribution. We first decorrelate the image
and then apply a region-based active contour whose motion is derived from an appropri-
ate parametric distribution for maximum likelihood image segmentation. We consider
zero-mean complex Gaussian, Rayleigh, and Fisher-Tippett flows, which are designed
to model fully formed speckle in the in-phase/quadrature (IQ), envelope detected, and
display (log compressed) images, respectively. We present experimental results demon-
strating the effectiveness of our method, and compare the results to other parametric
and non-parametric active contours
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
ROAM: a Rich Object Appearance Model with Application to Rotoscoping
Rotoscoping, the detailed delineation of scene elements through a video shot,
is a painstaking task of tremendous importance in professional post-production
pipelines. While pixel-wise segmentation techniques can help for this task,
professional rotoscoping tools rely on parametric curves that offer the artists
a much better interactive control on the definition, editing and manipulation
of the segments of interest. Sticking to this prevalent rotoscoping paradigm,
we propose a novel framework to capture and track the visual aspect of an
arbitrary object in a scene, given a first closed outline of this object. This
model combines a collection of local foreground/background appearance models
spread along the outline, a global appearance model of the enclosed object and
a set of distinctive foreground landmarks. The structure of this rich
appearance model allows simple initialization, efficient iterative optimization
with exact minimization at each step, and on-line adaptation in videos. We
demonstrate qualitatively and quantitatively the merit of this framework
through comparisons with tools based on either dynamic segmentation with a
closed curve or pixel-wise binary labelling
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