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Extraction of disease area from retinal optical coherence tomography images using three dimensional regional Statistics

We propose a new extract on method of the macular disease area in the human retinal layer from OCT images using three dimensional regional statistics. In previous researches, we extracted disease area by using the mean and standard deviation of the two dimensional disease part pointed out by a clini...

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Main Authors: Mohd Fadzil, Abdul Kadir, Nakahara, I.a, Tsuruoka, S.c, Takase, H.a, Kawanaka, H.a, Okuyama, F.d, Matsubara, H
Format: Conference or Workshop Item
Language:English
Published: 2014
Subjects:
QA75 Electronic computers. Computer science
T Technology (General)
Online Access:http://eprints.unisza.edu.my/358/1/FH03-FIK-15-02473.jpg
http://eprints.unisza.edu.my/358/
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spelling my-unisza-ir.3582020-10-21T06:50:41Z http://eprints.unisza.edu.my/358/ Extraction of disease area from retinal optical coherence tomography images using three dimensional regional Statistics Mohd Fadzil, Abdul Kadir Nakahara, I.a Tsuruoka, S.c Takase, H.a Kawanaka, H.a Okuyama, F.d Matsubara, H QA75 Electronic computers. Computer science T Technology (General) We propose a new extract on method of the macular disease area in the human retinal layer from OCT images using three dimensional regional statistics. In previous researches, we extracted disease area by using the mean and standard deviation of the two dimensional disease part pointed out by a clinical doctor. However, the previous method cannot extract disease area for some disease OCT images precisely. In this paper, we propose a new extraction method of the disease area using three dimensional regional statistics. We use a set of 128 images (3D-0CT image) consisted of 2 dimensional OCT retinal image about one retina of a patient. The regional mean and regional standard deviation of gray level are calculated in the three dimensional region of interest (ROI, 125 (=5 x 5 x 5) pixels) in the abnormal area pointed by a clinical doctor. These values are compared with every ROI in the abnormal area to extract the disease area, and the proposal system measures the volume of the disease area. We apply the proposed method to OCT images of 5 patients with retinal diseases. As a result. we can measure the volume of the abnormal area with 80.7% average accuracy. 2014 Conference or Workshop Item NonPeerReviewed image en http://eprints.unisza.edu.my/358/1/FH03-FIK-15-02473.jpg Mohd Fadzil, Abdul Kadir and Nakahara, I.a and Tsuruoka, S.c and Takase, H.a and Kawanaka, H.a and Okuyama, F.d and Matsubara, H (2014) Extraction of disease area from retinal optical coherence tomography images using three dimensional regional Statistics. In: 17th kes2013, 09-11 September 2013, japan.
institution Universiti Sultan Zainal Abidin
building UNISZA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Sultan Zainal Abidin
content_source UNISZA Institutional Repository
url_provider https://eprints.unisza.edu.my/
language English
topic QA75 Electronic computers. Computer science
T Technology (General)
spellingShingle QA75 Electronic computers. Computer science
T Technology (General)
Mohd Fadzil, Abdul Kadir
Nakahara, I.a
Tsuruoka, S.c
Takase, H.a
Kawanaka, H.a
Okuyama, F.d
Matsubara, H
Extraction of disease area from retinal optical coherence tomography images using three dimensional regional Statistics
description We propose a new extract on method of the macular disease area in the human retinal layer from OCT images using three dimensional regional statistics. In previous researches, we extracted disease area by using the mean and standard deviation of the two dimensional disease part pointed out by a clinical doctor. However, the previous method cannot extract disease area for some disease OCT images precisely. In this paper, we propose a new extraction method of the disease area using three dimensional regional statistics. We use a set of 128 images (3D-0CT image) consisted of 2 dimensional OCT retinal image about one retina of a patient. The regional mean and regional standard deviation of gray level are calculated in the three dimensional region of interest (ROI, 125 (=5 x 5 x 5) pixels) in the abnormal area pointed by a clinical doctor. These values are compared with every ROI in the abnormal area to extract the disease area, and the proposal system measures the volume of the disease area. We apply the proposed method to OCT images of 5 patients with retinal diseases. As a result. we can measure the volume of the abnormal area with 80.7% average accuracy.
format Conference or Workshop Item
author Mohd Fadzil, Abdul Kadir
Nakahara, I.a
Tsuruoka, S.c
Takase, H.a
Kawanaka, H.a
Okuyama, F.d
Matsubara, H
author_facet Mohd Fadzil, Abdul Kadir
Nakahara, I.a
Tsuruoka, S.c
Takase, H.a
Kawanaka, H.a
Okuyama, F.d
Matsubara, H
author_sort Mohd Fadzil, Abdul Kadir
title Extraction of disease area from retinal optical coherence tomography images using three dimensional regional Statistics
title_short Extraction of disease area from retinal optical coherence tomography images using three dimensional regional Statistics
title_full Extraction of disease area from retinal optical coherence tomography images using three dimensional regional Statistics
title_fullStr Extraction of disease area from retinal optical coherence tomography images using three dimensional regional Statistics
title_full_unstemmed Extraction of disease area from retinal optical coherence tomography images using three dimensional regional Statistics
title_sort extraction of disease area from retinal optical coherence tomography images using three dimensional regional statistics
publishDate 2014
url http://eprints.unisza.edu.my/358/1/FH03-FIK-15-02473.jpg
http://eprints.unisza.edu.my/358/
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score 13.211869

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