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

Abstract

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.

DeepLab and Bias Field Correction Based Automatic Cone Photoreceptor Cell Identification with Adaptive Optics Scanning Laser Ophthalmoscope Images

“…To confirm the effectiveness of the proposed algorithm for cone photoreceptor cell identification, we evaluated its identification performance regarding three measures, 5 Wireless Communications and Mobile Computing namely, precision, recall, and F1 score, with respect to the manual identification results taken as reference. The overall precision, recall, and F1 score for identification are listed in Table 1, where the values are compared with those of several algorithms [15,18,25,26]. The proposed algorithm achieves accurate cone photoreceptor cell identification, outperforming the comparison algorithm [18,25,26] except the graph theory-based algorithm [15] which is often referred to as ground truthing cone photoreceptor cell identification but needs a large amount of computing and complex implementation.…”

“…Through thresholding, cone photoreceptor cells were identified in two steps. In detail, the contours of the segmentation results were extracted using function find Contours of OpenCV, and the centroids of the areas inside [18] 93.2% 96.6% 94.9% K-means clustering based algorithm [26] 93.4% 95.2% 94.3% Superpixels based algorithm [25] 80.1% 93.5% 86.3%…”

“…Therefore, semiautomatic and automatic algorithms for cone photoreceptor-cell identification have been devised [12][13][14][15][16][17][18][19][20][21][22][23][24][25][26]. They can be nonlearning-based algorithms [12][13][14][15][16][17][18], supervised-learning algorithms [19][20][21][22][23], and unsupervisedlearning algorithms [24][25][26]. Among them, supervised deep-learning algorithms have achieved the highest accuracy, thus, being a promising research direction given their potentially high performance.…”

A New Watershed Algorithm for Pothole Image Segmentation

“…The segmentation of the perforated road image using the graph cut method yields an accuracy of 81.4% (Vigneshwar & Hema Kumar, 2017) for the classification of potholed roads using the Support vector machine method (Yousaf et al, 2018). Research that has developed watershed methods include image segmentation using the morphological watershed method (Wenjuan et al, 2019;Román et al, 2020;Chen et al, 2020). Image segmentation using the transformation watershed method (Yang et al, 2020;Xu et al, 2020).…”

Retinal vessel segmentation via deep hierarchical semantic segmentation and closing operation

“…The segmentation of retinal structures in retinal images is an important processing task [1][2][3][4][5] . It includes segmentation of photoreceptor cells [1] , leakage [2] , and vessels [3][4][5] .…”