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Proceedings Paper

Fast detection of vascular plaque in optical coherence tomography images using a reduced feature set
Author(s): Ammu Prakash; Mariano Ocana Macias; Mark Hewko; Michael Sowa; Sherif Sherif
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Paper Abstract

Optical coherence tomography (OCT) images are capable of detecting vascular plaque by using the full set of 26 Haralick textural features and a standard K-means clustering algorithm. However, the use of the full set of 26 textural features is computationally expensive and may not be feasible for real time implementation. In this work, we identified a reduced set of 3 textural feature which characterizes vascular plaque and used a generalized Fuzzy C-means clustering algorithm. Our work involves three steps: 1) the reduction of a full set 26 textural feature to a reduced set of 3 textural features by using genetic algorithm (GA) optimization method 2) the implementation of an unsupervised generalized clustering algorithm (Fuzzy C-means) on the reduced feature space, and 3) the validation of our results using histology and actual photographic images of vascular plaque. Our results show an excellent match with histology and actual photographic images of vascular tissue. Therefore, our results could provide an efficient pre-clinical tool for the detection of vascular plaque in real time OCT imaging.

Paper Details

Date Published: 5 March 2018
PDF: 4 pages
Proc. SPIE 10591, 2nd Canterbury Conference on OCT with Emphasis on Broadband Optical Sources, 105910P (5 March 2018); doi: 10.1117/12.2283017
Show Author Affiliations
Ammu Prakash, Univ. of Manitoba (Canada)
Mariano Ocana Macias, Univ. of Manitoba (Canada)
Mark Hewko, National Research Council Canada (Canada)
Michael Sowa, National Research Council Canada (Canada)
Sherif Sherif, Univ. of Manitoba (Canada)


Published in SPIE Proceedings Vol. 10591:
2nd Canterbury Conference on OCT with Emphasis on Broadband Optical Sources
Adrian Podoleanu; Ole Bang, Editor(s)

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