Chinese Optics Letters, Volume. 18, Issue 10, 101701(2020)
Automated superpixels-based identification and mosaicking of cone photoreceptor cells for adaptive optics scanning laser ophthalmoscope
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Fig. 1. Diagram depicting the image processing of the proposed algorithmic steps.
Fig. 2. Example of image denoising: (a) before denoising and (b) after denoising.
Fig. 3. Cone photoreceptor cell number estimation: (a) denoised image, (b) power of discrete Fourier transform (DFT) of a log10 compressed, (c) averaged slice of (b), fitted curve in red, and (d) subtraction outcome of fitted curve (highlighted in red) from the blue curve in (c).
Fig. 4. Simple linear iterative clustering (SLIC) superpixels segmentation: (a) original image patch and (b) segmented image with oversegmentation.
Fig. 6. Example of superpixels merging outcome: (a) before merging and (b) after merging.
Fig. 7. Performance of the proposed method: (a) input AO-SLO image, (b) identification of cells and segmented image, and (c) mosaic image.
Table 1. Evaluation of the Effectiveness of Cell Identification: Overall Precision, Recall, and F1-Score Outcomes
View tableView in ArticleTable 1. Evaluation of the Effectiveness of Cell Identification: Overall Precision, Recall, and F1-Score Outcomes
Precision Percentage of actual cells in identified cells 77.3% Recall Percentage of actual cells identified 95.2% F1-score 85.3%
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Yiwei Chen, Yi He, Jing Wang, Wanyue Li, Lina Xing, Feng Gao, Guohua Shi, "Automated superpixels-based identification and mosaicking of cone photoreceptor cells for adaptive optics scanning laser ophthalmoscope," Chin. Opt. Lett. 18, 101701 (2020)
Paper Information
Category: Biomedical Optics
Received: Mar. 21, 2020
Accepted: Jun. 1, 2020
Published Online: Aug. 5, 2020
The Author Email: Guohua Shi (ghshi_lab@126.com)
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