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13 June 2022 Residues in Succession U-Net for Fast and Efficient Segmentation
Aqsa Sultana, Vijayan K. Asari, Theus Aspiras
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Proceedings Volume PC12101, Pattern Recognition and Tracking XXXIII; PC1210107 (2022) https://doi.org/10.1117/12.2634286
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States
Abstract
We propose a modified U-Net architecture incorporating the residues from successive layers for the extraction of features in subsequent layers. The new Residues in Succession U-Net model is evaluated for blood vessel segmentation in retinal images on a benchmark expert-annotated dataset viz. Structured Analysis of Retina (STARE). The testing and evaluation results shows improved performance when compared to U-Net and R2U-Net in the same experimentation setup. A nonlinear image enhancement strategy is employed to improve the fine details in the images so that the network will be able to capture more information in further processing.
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Aqsa Sultana, Vijayan K. Asari, and Theus Aspiras "Residues in Succession U-Net for Fast and Efficient Segmentation", Proc. SPIE PC12101, Pattern Recognition and Tracking XXXIII, PC1210107 (13 June 2022); https://doi.org/10.1117/12.2634286
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KEYWORDS
Image segmentation
Image enhancement
Performance modeling
Blood vessels
Classification systems
Data processing
Feature extraction
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