22 research outputs found
Extraction of Airways with Probabilistic State-space Models and Bayesian Smoothing
Segmenting tree structures is common in several image processing
applications. In medical image analysis, reliable segmentations of airways,
vessels, neurons and other tree structures can enable important clinical
applications. We present a framework for tracking tree structures comprising of
elongated branches using probabilistic state-space models and Bayesian
smoothing. Unlike most existing methods that proceed with sequential tracking
of branches, we present an exploratory method, that is less sensitive to local
anomalies in the data due to acquisition noise and/or interfering structures.
The evolution of individual branches is modelled using a process model and the
observed data is incorporated into the update step of the Bayesian smoother
using a measurement model that is based on a multi-scale blob detector.
Bayesian smoothing is performed using the RTS (Rauch-Tung-Striebel) smoother,
which provides Gaussian density estimates of branch states at each tracking
step. We select likely branch seed points automatically based on the response
of the blob detection and track from all such seed points using the RTS
smoother. We use covariance of the marginal posterior density estimated for
each branch to discriminate false positive and true positive branches. The
method is evaluated on 3D chest CT scans to track airways. We show that the
presented method results in additional branches compared to a baseline method
based on region growing on probability images.Comment: 10 pages. Pre-print of the paper accepted at Workshop on Graphs in
Biomedical Image Analysis. MICCAI 2017. Quebec Cit
Segmentation of nerve bundles and ganglia in spine MRI using particle filters
14th International Conference, Toronto, Canada, September 18-22, 2011, Proceedings, Part IIIAutomatic segmentation of spinal nerve bundles that originate within the dural sac and exit the spinal canal is important for diagnosis and surgical planning. The variability in intensity, contrast, shape and direction of nerves seen in high resolution myelographic MR images makes segmentation a challenging task. In this paper, we present an automatic tracking method for nerve segmentation based on particle filters. We develop a novel approach to particle representation and dynamics, based on BĂ©zier splines. Moreover, we introduce a robust image likelihood model that enables delineation of nerve bundles and ganglia from the surrounding anatomical structures. We demonstrate accurate and fast nerve tracking and compare it to expert manual segmentation.National Institutes of Health (U.S.) (NAMIC award U54-EB005149)National Science Foundation (U.S.) (CAREER grant 0642971
Extracting Tree-structures in CT data by Tracking Multiple Statistically Ranked Hypotheses
In this work, we adapt a method based on multiple hypothesis tracking (MHT)
that has been shown to give state-of-the-art vessel segmentation results in
interactive settings, for the purpose of extracting trees. Regularly spaced
tubular templates are fit to image data forming local hypotheses. These local
hypotheses are used to construct the MHT tree, which is then traversed to make
segmentation decisions. However, some critical parameters in this method are
scale-dependent and have an adverse effect when tracking structures of varying
dimensions. We propose to use statistical ranking of local hypotheses in
constructing the MHT tree, which yields a probabilistic interpretation of
scores across scales and helps alleviate the scale-dependence of MHT
parameters. This enables our method to track trees starting from a single seed
point. Our method is evaluated on chest CT data to extract airway trees and
coronary arteries. In both cases, we show that our method performs
significantly better than the original MHT method.Comment: Accepted for publication at the International Journal of Medical
Physics and Practic
Segmentation of nerve bundles and ganglia in spine MRI using particle filters
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 41-44).Automatic segmentation of spinal nerve bundles originating within the dural sac and exiting the spinal canal is important for diagnosis and surgical planning. The variability in intensity, contrast, shape and direction of nerves seen in high resolution myelographic MR images makes segmentation a challenging task. In this thesis, we present an automatic tracking method for segmentation of nerve bundles based on particle filters. We develop a novel approach to flexible particle representation of tubular structures based on Bezier splines. We construct an appropriate dynamics to reflect the continuity and smoothness properties of real nerve bundles. Moreover, we introduce a robust image likelihood model that enables delineation of nerve bundles and ganglia from the surrounding anatomical structures. We evaluate the results by comparing them to expert manual segmentation, and we demonstrate accurate and fast nerve tracking.by Adrian Vasile Dalca.S.M
Automatic Segmentation and Measurement of Vasculature in Retinal Fundus Images Using Probabilistic Formulation
The automatic analysis of retinal blood vessels plays an important role in the computer-aided diagnosis. In this paper, we introduce a probabilistic tracking-based method for automatic vessel segmentation in retinal images. We take into account vessel edge detection on the whole retinal image and handle different vessel structures. During the tracking process, a Bayesian method with maximum a posteriori (MAP) as criterion is used to detect vessel edge points. Experimental evaluations of the tracking algorithm are performed on real retinal images from three publicly available databases: STARE (Hoover et al., 2000), DRIVE (Staal et al., 2004), and REVIEW (Al-Diri et al., 2008 and 2009). We got high accuracy in vessel segmentation, width measurements, and vessel structure identification. The sensitivity and specificity on STARE are 0.7248 and 0.9666, respectively. On DRIVE, the sensitivity is 0.6522 and the specificity is up to 0.9710
Evaluating perceptual maps of asymmetries for gait symmetry quantification and pathology detection
Le mouvement de la marche est un processus essentiel de l'activité
humaine et aussi le résultat de nombreuses interactions collaboratives
entre les systèmes neurologiques, articulaires et
musculo-squelettiques fonctionnant ensemble efficacement. Ceci
explique pourquoi une analyse de la marche est aujourd'hui de plus en
plus utilisée pour le diagnostic (et aussi la prévention) de
différents types de maladies (neurologiques, musculaires,
orthopédique, etc.). Ce rapport présente une nouvelle méthode pour
visualiser rapidement les diffĂ©rentes parties du corps humain liĂ©es Ă
une possible asymétrie (temporellement invariante par translation)
existant dans la démarche d'un patient pour une possible utilisation
clinique quotidienne. L'objectif est de fournir une méthode à la fois
facile et peu dispendieuse permettant la mesure et l'affichage visuel,
d'une manière intuitive et perceptive, des différentes parties
asymétriques d'une démarche. La méthode proposée repose sur
l'utilisation d'un capteur de profondeur peu dispendieux (la Kinect)
qui est très bien adaptée pour un diagnostique rapide effectué dans de
petites salles mĂ©dicales car ce capteur est d'une part facile Ă
installer et ne nécessitant aucun marqueur. L'algorithme que nous
allons présenter est basé sur le fait que la marche saine possède des
propriétés de symétrie (relativement à une invariance temporelle) dans
le plan coronal.The gait movement is an essential process of the human activity and
also the result of coordinated effort between the neurological,
articular and musculoskeletal systems. This motivates why gait
analysis is important and also increasingly used nowadays for the
(possible early) diagnosis of many different types (neurological,
muscular, orthopedic, etc.) of diseases. This paper introduces a
novel method to quickly visualize the different parts of the body
related to an asymmetric movement in the human gait of a patient for
daily clinical. The goal is to provide a cheap and easy-to-use method
to measure the gait asymmetry and display results in a perceptually
relevant manner. This method relies on an affordable consumer depth
sensor, the Kinect. The Kinect was chosen because this device is
amenable for use in small, confined area, like a living room. Also,
since it is marker-less, it provides a fast non-invasive diagnostic.
The algorithm we are going to introduce relies on the fact that a
healthy walk has (temporally shift-invariant) symmetry properties in
the coronal plane
Vessel tractography using an intensity based tensor model
In the last decade, CAD (Coronary Artery Disease) has been the leading cause of death worldwide [1]. Extraction of arteries is a crucial step for accurate visualization, quantification, and tracking of pathologies. However, coronary artery segmentation is one of the most challenging problems in medical image analysis, since arteries are complex tubular structures with bifurcations, and have possible pathologies. Moreover, appearance of blood vessels and their geometry can be perturbed by stents, calcifications and pathologies such as stenosis. Besides, noise, contrast and resolution artifacts can make the problem more challenging. In this thesis, we present a novel tubular structure segmentation method based on an intensity-based tensor that fits to a vessel, which is inspired from diffusion tensor image (DTI) modeling. The anisotropic tensor inside the vessel drives the segmentation analogously to a tractography approach in DTI. Our model is initialized with a single seed point and it is capable of capturing whole vessel tree by an automatic branch detection algorithm. The centerline of the vessel as well as its thickness is extracted. We demonstrate the performance of our algorithm on 3 complex tubular structured synthetic datasets, and on 8 CTA (Computed Tomography Angiography) datasets (from Rotterdam Coronary Artery Algorithm Evaluation Framework) for quantitative validation. Additionally, extracted arteries from 10 CTA volumes are qualitatively evaluated by a cardiologist expert's visual scores