Mohd Fadzil, Abdul Kadir and Abd.Rasid, Mamat and Azrul Amri, Jamal (2015) Extraction of macular disease area using regional statistics technique for human retinal Optical Coherence Tomography (OCT) image. Applied Mathematical Sciences, 9 (129). pp. 6437-6448. ISSN 1312885X [P]
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Abstract
Optical Coherence Tomography (OCT) has emerged as a new technology that enables high-resolution cross-sectional images of the retina for identifying, and quantitatively assessing of the retina disease. Quantitative information of retina is needed for tracking progression of ocular disease and evaluates the efficacy of treatment. In this paper, we propose a new border tracking procedure using regional statistics (BTPRS) to extract an abnormal area that specified by medical doctor. This procedure uses a combination of regional statistics and border tracking method. The objectives of this research are to extract the abnormal area in human retina from optical coherence tomography images and to improve the extraction percentage. This research uses 128 pieces of 2 dimensional OCT retinal image of one drusenpatient, and 128 pieces of 2dimensional OCT retinal image of a diabetic macular edema (DME) patient. The part of the diseases are specified by a medical doctor. Results show that the regional statistic border tracking method provided the highest extraction of rate percentage and can extract the abnormal area in both conditions, white and black. In this paper, we will focus on the abnormal area at macular part. This research will provide more useful information to medical doctor and patient for informed consent. We hope that this procedure will be added in the commercial OCT unit to evaluate the degree of disease and response to the treatment.
Item Type: | Article |
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Uncontrolled Keywords: | Optical Coherence Tomography; Image Scanning; Border Tracking; Regional Statistics |
Subjects: | R Medicine > R Medicine (General) |
Divisions: | Faculty of Informatics & Computing |
Depositing User: | Syahmi Manaf |
Date Deposited: | 13 Sep 2022 04:51 |
Last Modified: | 13 Sep 2022 04:51 |
URI: | http://eprints.unisza.edu.my/id/eprint/7136 |
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