Research Overview:
Cardiovascular Disease (CVD) is one of the leading causes of death both in United States and all around
the globe. The National Heart Lung and Blood Institute's 2007 disease statistics reported that (in 2004)
872,000 deaths or 36% of all the deaths in United States were due to cardiovascular disease.
One of the primary causes of CVD is coronary artery atherosclerosis, also popularly known as coronary
artery disease (CAD), a condition that refers to a narrowing and hardening of the coronary arteries
that supply blood to the heart. This occurs due to atherosclerosis, a disease in which scattered
lesions called atherosclerotic plaques (a mixture of fatty substances, cholestrol, cellular wastes,
calcium, and the blood clotting material called fibrin) build up on the inner walls of the coronary
arteries. Such a condition progressively obstructs the flow of blood through the coronary arteries and
when it worsens may eventually lead to a heart attack.
Recent studies have established that the presence of calcified
coronary plaques as detected from non-contrast computed tomography (CT) data has a significant predictive
value for CAD in both symptomatic and asymptomatic patients. To that end, several risk scores have been
developed to quantify the amount of coronary artery calcium (CAC) based on the data collected by CT.
However, inspite of the vast amount of CAD-related information available from CT, only a small fraction
of it is being used in existing risk scoring strategies. Additionally, most of these scores were devised
based on small or diseased populations without rigorous statistical validation. This can be attributed to
the lack of robust image analysis methods for the automated extraction of CAD-related information from
non-contrast CT imagery. This is precisely the issue that we are trying to address in our ongoing
research.
The overall goal of our research is to develop the fundamental set of computational tools that will
pave the way for the automated extraction of a variety of CAD-related information from non-contrast
CT data.
Abstract:
The inner thoracic region consists of several important
anatomical structures and an accurate delineation of this
region is an essential step for various biomedical image analysis
applications. In this paper, we present a fully automatic graphbased
method for the delineation of the inner thoracic region
in non-contrast cardiac CT data. In particular, we reformulate
the problem of delineating the inner thoracic region as an
optimal surface segmentation problem, the solution to which
is obtained by computing the minimum-cost closed set in a
node-weighted directed graph. Comparing the results obtained
using our method with manual segmentations performed by
an expert on non-contrast cardiac CT scans of 20 randomly
selected patients indicated an overlap of 99.1 +/- 0:2%.
Outreach Material:
Associated Publications:
-
D.R. Chittajallu, P. Balança, and I.A. Kakadiaris, "Automatic delineation of the inner
thoracic region in non-contrast CT data," in Proc. 31st International Conference of the
IEEE Engineering in Medicine and Biology Society (EMBC), Minneapolis, MN, Sep. 2-6 2009.
Abstract:
Image segmentation is, in general, an ill-posed problem
and additional constraints need to be imposed in order
to achieve the desired result. Particularly in the field of
medical image segmentation, a significant amount of prior
knowledge is available that can be used to constrain the solution
space of the segmentation problem. However, most
of this prior knowledge is, in general, vague or imprecise
in nature, which makes it very difficult to model. This is the
problem that is addressed in this paper. Specifically, in this
paper, we present Fuzzy-Cuts, a novel, knowledge-driven,
graph-based method for medical image segmentation. We
cast the problem of image segmentation as the Maximum
A Posteriori (MAP) estimation of a Markov Random Field
(MRF) which, in essence, is equivalent to the minimization
of the corresponding Gibbs energy function. Considering
the inherent imprecision that is common in the a priori
description of objects in medical images, we propose a
fuzzy theoretic model to incorporate knowledge-driven constraints
into the MAP-MRF formulation. In particular, we
focus on prior information about the object's location, appearance
and spatial connectivity to a known seed region
inside the object. To that end, we introduce fuzzy connectivity
and fuzzy location priors that are used in combination
to define the first-order clique potential of the Gibbs energy
function. In our experiments, we demonstrate the application
of the proposed method to the challenging problem of
heart segmentation in non-contrast computed tomography
(CT) data.
Outreach Material:
Associated Publications:
-
D. R. Chittajallu, G. Brunner, U. Kurkure, R. Yalamanchili, and I. A. Kakadiaris, "Fuzzy-cuts:
A knowledge-driven graph-based method for medical image segmentation," in Proc. IEEE Computer Society
Conference on Computer Vision and Pattern Recognition (CVPR) Miami Beach, FL, 2009.
