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About A Novel Retinal Segmentation Based On DNNR Model
This document describes a novel retinal segmentation method based on a deep neural network regression (DNNR) model. The proposed method takes intensity, gradient, and adaptive normalized intensity score (ANIS) of an image segment as features for the DNNR to learn and predict retinal boundary pixels. Evaluating the method on 114 images, it achieved accurate segmentation of eight boundaries in around 10.596 seconds per image, requiring only 30 seconds of training per boundary. The Dice similarity coefficient between manual and automatic segmentation was 0.966 on average, indicating a sub-pixel difference of 0.612 pixels. The method operates robustly on OCT images despite intensity variations, low contrast regions, speckle noise, and blood vessels.
Detailed Information
| Author: | ['V MERIN SHOBI'] |
|---|---|
| Publication Year: | 2021 |
| Pages: | 54 |
| Language: | English |
| Format: | |
| Price: | FREE |
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