Automated Identification of Photoreceptor Cones Using Multi-scale Modelling and Normalized Cross-Correlation

Turpin, Alan; Morrow, Philip; Scotney, Bryan; Anderson, R. H.; Wolsley, Clive

doi:10.1007/978-3-642-24085-0_51

Cited by 14 publications

(14 citation statements)

References 8 publications

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“…Several algorithms for cone detection and counting have been described [10,11,12,13,14,15,16]. As mentioned, the performance of detection methods has always been estimated by comparing with manual labelling operated by an expert or by the authors themselves.…”

Section: Introductionmentioning

confidence: 99%

Performance analysis of cone detection algorithms

Mariotti

Devaney²

2015

J. Opt. Soc. Am. A

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Many algorithms have been proposed to help clinicians evaluate cone density and spacing, as these may be related to the onset of retinal diseases. However, there has been no rigorous comparison of the performance of these algorithms. In addition, the performance of such algorithms is typically determined by comparison with human observers. Here we propose a technique to simulate realistic images of the cone mosaic. We use the simulated images to test the performance of three popular cone detection algorithms, and we introduce an algorithm which is used by astronomers to detect stars in astronomical images. We use Free Response Operating Characteristic (FROC) curves to evaluate and compare the performance of the four algorithms. This allows us to optimize the performance of each algorithm. We observe that performance is significantly enhanced by up-sampling the images. We investigate the effect of noise and image quality on cone mosaic parameters estimated using the different algorithms, finding that the estimated regularity is the most sensitive parameter.

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Section: Introductionmentioning

confidence: 99%

Performance analysis of cone detection algorithms

Mariotti

Devaney²

2015

J. Opt. Soc. Am. A

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“…Although manual methods for segmentation and identification of cone photoreceptor cells are accurate and reliable, the associated labor costs and time required to perform them are excessive. Therefore, several studies aimed at the development of semi-automated and automated methods for segmentation and identification of cone photoreceptor cells have been conducted; these methods include non-learning [12][13][14][15][16], supervised-learning- [17][18][19], and unsupervised-learning-based methods [20]. Although these methods achieve high accuracy in cone photoreceptor cell segmentation and identification in healthy retinas, there is no study that confirms the high accuracy of the existing methods for all types of eye diseases.…”

Section: Introductionmentioning

confidence: 99%

Automated Cone Photoreceptor Cell Segmentation and Identification in Adaptive Optics Scanning Laser Ophthalmoscope Images Using Morphological Processing and Watershed Algorithm

Chen¹,

He²,

Wang³

et al. 2020

IEEE Access

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Geometrical analysis of cone photoreceptor cells is important not only for ophthalmic diagnosis, but also for research on eye diseases. In this study, an automated cone photoreceptor cell segmentation and identification method based on morphological processing and watershed algorithm is presented for adaptive optics scanning laser ophthalmoscope images. Our method includes steps for image denoising, rough segmentation, fine segmentation, small region removal, and identification. The effectiveness of the proposed method was confirmed by comparing its results with those obtained manually yielding precision, recall, and F1-score values of 93.6%, 98.0% and 95.8%, respectively. The performance of our method is further verified by processing images with different cone photoreceptor cell densities from healthy retina and an image from an eye with diabetic retinopathy. The experimental results show that our algorithm achieved high accuracy in cone photoreceptor cell segmentation and identification in healthy retinas as well as in retina with diabetic retinopathy. INDEX TERMS Retinal images, adaptive optics scanning laser ophthalmoscope, image segmentation.

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“…Several automatic or semi-automatic methods have been proposed to create a faster and more consistent cone detection process. Some methods are based on standard image analysis techniques: image histogram analysis,7 multi-scale modelling and normalized cross-correlation,8 a circular Hough transform,9 and multiscale circular voting 10. In recent years, machine learning methods have also been applied to this problem.…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection of Cone Photoreceptors With Fully Convolutional Networks

Hamwood

Alonso‐Caneiro

Sampson

et al. 2019

Trans. Vis. Sci. Tech.

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Purpose: To develop a fully automatic method, based on deep learning algorithms, for determining the locations of cone photoreceptors within adaptive optics scanning laser ophthalmoscope images and evaluate its performance against a dataset of manually segmented images.Methods: A fully convolutional network (FCN) based on U-Net architecture was used to generate prediction probability maps and then used a localization algorithm to reduce the prediction map to a collection of points. The proposed method was trained and tested on two publicly available datasets of different imaging modalities, with Dice overlap, false discovery rate, and true positive reported to assess performance.Results: The proposed method achieves a Dice coefficient of 0.989, true positive rate of 0.987, and false discovery rate of 0.009 on the first confocal dataset; and a Dice coefficient of 0.926, true positive rate of 0.909, and false discovery rate of 0.051 on the second split detector dataset. Results compare favorably with a previously proposed method, but this method provides quicker (25 times faster) evaluation performance. Conclusions:The proposed FCN-based method demonstrates that deep learning algorithms can achieve accurate cone localizations, almost comparable to a human expert, while labeling the images.Translational Relevance: Manual cone photoreceptor identification is a timeconsuming task due to the large number of cones present within a single image; using the proposed FCN-based method could support the image analysis task, drastically reducing the need for manual assessment of the photoreceptor mosaic.

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Automated Identification of Photoreceptor Cones Using Multi-scale Modelling and Normalized Cross-Correlation

Cited by 14 publications

References 8 publications

Performance analysis of cone detection algorithms

Performance analysis of cone detection algorithms

Automated Cone Photoreceptor Cell Segmentation and Identification in Adaptive Optics Scanning Laser Ophthalmoscope Images Using Morphological Processing and Watershed Algorithm

Automatic Detection of Cone Photoreceptors With Fully Convolutional Networks

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