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Ranjitha Rajan Rajan, S.N. Kumar,
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- Assistant Professor,, Assistant Professor, Department of Electronics Communication Engineering, AmalJyothi College of Engineering,, Department of Electronics Communication Engineering, AmalJyothi College of Engineering, Koovappally, Kerala,, Koovappally, Kerala, India, India
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Abstract
nSegmenting retinal layers from Optical Coherence Tomography (OCT) pictures entails locating and separating different retinal layers to offer comprehensive anatomical and pathological information. Age-related macular degeneration, diabetic retinopathy, and glaucoma are among the retinal illnesses for which this procedure is crucial for diagnosis and follow-up. By utilizing preprocessing techniques to improve image quality and applying advanced algorithms—such as intensity-based, gradient-based, and texture-based methods—alongside deep learning approaches, clinicians can accurately measure the thickness and volume of retinal layers. This segmentation enables precise assessment of retinal health, supports surgical planning, and facilitates the evaluation of treatment effectiveness, ultimately enhancing patient outcomes through early and accurate diagnosis. The proposed method begins with preprocessing steps in MATLAB to enhance image quality. These include edge detection, histogram equalization for contrast enhancement, and median filtering for noise reduction. The segmented layers are then post-processed to refine the boundaries and eliminate any artifacts. This approach possibly improves patient outcomes and streamlines clinical workflows by increasing diagnostic accuracy and assisting in the early detection and management of retinal disorders. The use of MATLAB codes for this aprovideon provides a flexible and accessible platform for further research and development in retinal image analysis.
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Keywords: Segmentation, OCT, retinal layers, deep learning, early diagnosis
n[if 424 equals=”Regular Issue”][This article belongs to Trends in Opto-electro & Optical Communication(toeoc)]
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References
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| Volume | 14 | |
| [if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] | 01 | |
| Received | July 16, 2024 | |
| Accepted | July 29, 2024 | |
| Published | August 16, 2024 |
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