Abstract
Cardiovascular disease is one of the leading causes of death in Canada. Atherosclerosis is
considered the primary cause for cardiovascular disease. Optical coherence tomography (OCT)
provides a means to minimally invasive imaging and assessment of textural features of
atherosclerotic plaque. However, detecting atherosclerotic plaque by visual inspection from
Optical Coherence Tomography (OCT) images is usually difficult. Therefore we
developed unsupervised segmentation algorithms to automatically detect atherosclerosis plaque
from OCT images. We used three different clustering methods to identify atherosclerotic plaque
automatically from OCT images. Our method involves data preprocessing of raw OCT images,
feature selection and texture feature extraction using the Spatial Gray Level Dependence Matrix
method (SGLDM), and the application of three different clustering techniques: K-means, Fuzzy
C-means and Gustafson-Kessel algorithms to segment the plaque regions from OCT images and
to map the cluster regions (background, vascular tissue, OCT degraded signal region and
Atherosclerosis plaque) from the feature-space back to the original preprocessed OCT image.
We validated our results by comparing our segmented OCT images with actual photographic
images of vascular tissue with plaque. / October 2015
| Identifer | oai:union.ndltd.org:MANITOBA/oai:mspace.lib.umanitoba.ca:1993/30786 |
| Date | 14 September 2015 |
| Creators | Ocaña Macias Mariano |
| Contributors | Sherif, Sherif (Electrical and Computer Engineering), Major, Arkady (Electrical and Computer Engineering) Hewko, Mark (Biosystems Engineering) |
| Source Sets | University of Manitoba Canada |
| Detected Language | English |
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