DAMAYANTI, HERANTI REZA and Erwin, Erwin (2020) SEGMENTASI PEMBULUH DARAH PADA CITRA RETINA DENGAN METODE ITERATIVE SELF-ORGANIZING DATA ANALYSIS. Undergraduate thesis, Sriwijaya University.
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Abstract
The retinal fundus is the surface in the eye that is connected to the lens. Identification of the disease requires several parts of the retinal fundus, one of which is blood vessels. Blood vessels are part of the circulatory system that serves to supply blood to the retinal region. This study proposes a method for segmenting blood vessels in retinal images with Iterative Self-Organizing Data Analysis (ISODATA). The first step is pre-processing with the input image changed to Green Channel, then increasing contrast with Contrast Limited Adaptive Histogram Equalization (CLAHE), then the Enhancement Filter process. For the segmentation process, the method used is the Iterative Self-Organizing Data Analysis (ISODATA). For the post-processing stage with the steps of eliminating small pixels, masking and elimination of noise edges, median filter, and finally the closing morphology of the dataset used in this study are DRIVE and STARE. The average results obtained for the STARE dataset with an accuracy of 94.71%, sensitivity 58.47%, specifications 98.33%, precision 79.21%, F1 score 67.00% (comparison with Adam Hoover's ground truth) and accuracy 92, 83%, sensitivity 48.25%, specifications 98.83%, precision 85.90%, and F1 score 61.49% (comparison with Valentina Kouznetsova's ground truth). As for the DRIVE dataset, the results obtained were accuracy 96.29%, sensitivity 55.82%, spesifications 98.20%, precision 89, 47%, and F1 score 68.60%.
Item Type: | Thesis (Undergraduate) |
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Uncontrolled Keywords: | Segmentasi, Pembuluh Darah, Fundus Retina, Enhancement Filter, ISODATA |
Subjects: | Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation. |
Divisions: | 09-Faculty of Computer Science > 56201-Computer Systems (S1) |
Depositing User: | Users 7046 not found. |
Date Deposited: | 11 Aug 2020 04:02 |
Last Modified: | 11 Aug 2020 04:02 |
URI: | http://repository.unsri.ac.id/id/eprint/32465 |
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