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Proceedings Paper

Automated multimodality concurrent classification for segmenting vessels in 3D spectral OCT and color fundus images
Author(s): Zhihong Hu; Michael D. Abràmoff; Meindert Niemeijer; Mona K. Garvin
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Paper Abstract

Segmenting vessels in spectral-domain optical coherence tomography (SD-OCT) volumes is particularly challenging in the region near and inside the neural canal opening (NCO). Furthermore, accurately segmenting them in color fundus photographs also presents a challenge near the projected NCO. However, both modalities also provide complementary information to help indicate vessels, such as a better NCO contrast from the NCO-aimed OCT projection image and a better vessel contrast inside the NCO from fundus photographs. We thus present a novel multimodal automated classification approach for simultaneously segmenting vessels in SD-OCT volumes and fundus photographs, with a particular focus on better segmenting vessels near and inside the NCO by using a combination of their complementary features. In particular, in each SD-OCT volume, the algorithm pre-segments the NCO using a graph-theoretic approach and then applies oriented Gabor wavelets with oriented NCO-based templates to generate OCT image features. After fundus-to-OCT registration, the fundus image features are computed using Gaussian filter banks and combined with OCT image features. A k-NN classifier is trained on 5 and tested on 10 randomly chosen independent image pairs of SD-OCT volumes and fundus images from 15 subjects with glaucoma. Using ROC analysis, we demonstrate an improvement over two closest previous works performed in single modal SD-OCT volumes with an area under the curve (AUC) of 0.87 (0.81 for our and 0.72 for Niemeijer's single modal approach) in the region around the NCO and 0.90 outside the NCO (0.84 for our and 0.81 for Niemeijer's single modal approach).

Paper Details

Date Published: 8 March 2011
PDF: 10 pages
Proc. SPIE 7962, Medical Imaging 2011: Image Processing, 796204 (8 March 2011); doi: 10.1117/12.877880
Show Author Affiliations
Zhihong Hu, The Univ. of Iowa (United States)
Michael D. Abràmoff, The Univ. of Iowa (United States)
Iowa City VA Medical Ctr. (United States)
Meindert Niemeijer, The Univ. of Iowa (United States)
Mona K. Garvin, The Univ. of Iowa (United States)
Iowa City VA Medical Ctr. (United States)

Published in SPIE Proceedings Vol. 7962:
Medical Imaging 2011: Image Processing
Benoit M. Dawant; David R. Haynor, Editor(s)

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