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Detection of the Aortic Lumen Using a Kmeans Clustering Approach
Bradley Wendorff
ECE 539 – Final Report
20/Dec/2013
Introduction (problem statement/motivation)
X-Ray Computed Tomography (CT) imaging is an extremely important diagnostic
tool used across multiple disciplines within the health care field. Acquisition of CT scans
consists of three steps: (1) injection of a radio-opaque dye into the bloodstream, (2) 2D
image acquisition and reconstruction of the sequence in 3D, and (3) creation of angiograms
using 3D reconstruction or re-projection of the 3D image in 2D.
CT scans give physicians the ability to view the deep tissues of their patients in 3dimensions after the planar slices are reconstructed. The human body is packed with
various types of tissues and buildups such as calcium deposits that appear as we age. Each
of these tissues appear differently on an X-ray film, because they are all of different
densities which is directly correlated to the amount that they attenuate the radiation as it
passes through the body. When physicians are viewing their patient’s imaging, it is
extremely important that their view of potential pathologies or sources of increased risk
factors are well accounted for.
Imaging has become a luxury, and a necessity for patients and physicians alike. Prior
to the steady advancement we’ve seen in medical imaging today, suspected ailments were
further investigated with the use of exploratory surgery, which could often do more harm
than aid if the physicians suspicions were incorrect. Additionally, these suspicions could
only be marginally confirmed if the pathology in question was superficial in the body, and
yielded some sort of sensation when palpated in the right area. Unfortunately, there were
numerous conditions that were not as trivial to diagnose with the use of superficial
palpations and description of symptoms, and many such conditions are native to the
vascular system.
Within the field of vascular surgery, it is often desired to view specific vessels of the
body, such as the aorta if a patient is undergoing endovascular surgery. The aorta is the
main artery that blood first enters when it leaves the heart, and peripheral branches
diverge from it to supply other tissues in the body. Given that the aorta is so deep within
our bodies, there are several layers of organs, bones, and other tissues impeding a
physicians view of it. In a contrast enhanced axial CT scan, the lumen of the vasculature
shows up as brightly, because the contrast increases blood’s attenuation to the radiation
passing though. The aortic lumen is shown below in figure 1.
Figure 1: This 2D axial image is an example of one
slice of a CT scan. The red arrow pointing to the
bright area signifies the aortic lumen.
Manual segmentation of images is a very time consuming, and tedious task (on the
scale of hours) for radiologists to complete, as it requires manual tracing of the region of
interest in a scan that can consist of over a thousand images. Not only is this very time
consuming, but it can also be very difficult depending on the quality of the scan, and
interpersonal inconsistencies can arise because manual tracing can be very subjective
based who is reading the scan. Thus, major efforts have been made to design algorithms
and computer software that limits segmentation bias, and ultimately increases consistency
and accuracy, as the computer is able to base its segmentation decisions down to the
individual pixel value.
The purpose of this project was to detect the aortic lumen in a series of CT scans,
and then reconstruct them in a 3D orientation as shown below in figure 2.
Figure 2: 3D reconstruction of
the aorta.
In order to pick out the aortic lumen in an axial slice of a CT scan, a K-means clustering
algorithm was implemented.
Work Performed
Axial CT images were imported into Matlab, and the RGB image data was extracted.
Matlab’s kmeans command was utilized on each image with random seed points, and the
resulting output showed a series of three segmented images, one of which showed only the
aorta and the spine against a black background.
Results
The images below show an example of the results from running the kmeans
clustering algorithm. Though the algorithm does not correctly assign cluster centers with
each iteration, it does do fairly well in picking out only the aortic lumen in the center image
in figure 3. With additional image processing, it would be fairly trivial to pick out the center
of the lumen when it is against the black background
Figure 3: Representation of the output. The center image shows the aorta brightly lit, with the
majority of the lumen included.
Discussion
Future work in detecting and segmenting the aorta will take several other design
considerations into account when designing the algorithm. First, the K-means clustering
that was used relied on random cluster centers to determine where clusters were made.
Though multiple iterations of clustering were completed on each image, there were
occasional images that turned up null for clusters. In order to mitigate this, a seed value
will be given to initiate the first center in a series of axial images, and each successive
image will learn and use the center from the previous image to begin clustering. This will
help to eliminate the null clusters, and lead to increased accuracy in picking out only the
aorta, and removing it from the rest of the image. Additionally, it would be advantageous to
outline the lumen of the aorta, and then calculate the circumference so that the data can be
used in determining the size and shape of each successive slice. An example of this concept
is shown below in figure 4, which is a screen shot taken from an industry standard
workstation, TeraRecon iNtuition.
Figure 4: Orthogonal image generated in
TeraRecon’s iNtuition workstation.
Each axial image is captured at a different moment in time than the adjacent images,
which can lead to differences in attenuation, and therefore the brightness of the lumen can
vary considerably based on the houndsfield units. Combining image processing techniques
that extract the pixel value data, with a learning algorithm based on the average pixel value
would strengthen the overall ability of the algorithm to pick out the lumen. Additionally,
pixel values could be used to include or exclude thrombus or calcium from images, as it can
be distracting for physicians when they are looking at the flow of a patient’s aorta.
References
S. Shiffman, G. D. Rubin, and S. Napel, Semiautomated editing of computed tomography
sections for visualization of vasculature, vol. 2707, SPIE, 1996.
http://www.siue.edu/~sumbaug/RetinalProjectPapers/Review%20of%20Blood%20Vess
el%20Extraction%20Techniques%20and%20Algorithms.pdf