A Study on Retinal Image Segmentation and Registration Methods

International Journal of Scientific Research in Computer Science Engineering and Information Technology ◽

10.32628/cseit21713 ◽

2021 ◽

pp. 25-33

Author(s):

B. Sivaranjani ◽

C. Kalaiselvi

Keyword(s):

Image Segmentation ◽

Retinal Image ◽

Template Matching ◽

Diagnosis And Treatment ◽

Retinal Images ◽

Fundus Images ◽

Image Preprocessing ◽

Fundus Camera ◽

Motion Parameters ◽

Retinal Image Segmentation

Diagnosis and treatment of several disorders affecting the retina and the choroid behind it require capturing a sequence of fundus images using the fundus camera. These images are to be processed for better diagnosis and planning of treatment. Retinal image template matching is greatly required to extract certain features that may help in diagnosis and treatment. Also registration of retinal images is very useful in extracting the motion parameters that help in composing a complete map for the retina as well as in retinal tracking. This paper introduces a survey for the image preprocessing, dimensionality reduction, template matching and registration techniques that were reported as being well for retinal images.

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Comparative Analysis of Improved of FCM Algorithms in the Segmentation of Retinal Blood Vessels

10.21203/rs.3.rs-886041/v1 ◽

2022 ◽

Author(s):

Imane Mehidi ◽

Djamel Eddine Chouaib Belkhiat ◽

Dalel Jabri

Keyword(s):

Image Segmentation ◽

Blood Vessels ◽

Retinal Image ◽

Color Image ◽

Region Of Interest ◽

Retinal Images ◽

Fundus Image ◽

Retinal Blood Vessels ◽

Two Phases ◽

Retinal Image Segmentation

Abstract The main purpose of identifying and locating the vessels of the retina is to specify the various tissues from the vascular structure of the retina (which could be differencied between wide or tight) of the background of the fundus image. There exist several segmentation techniques that are spreading to divide the retinal vessels, depending on the issues and complexity of the retinal images. Fuzzy c-means is one of the most often used algorithms for retinal image segmentation due to its effectiveness and speed. This paper analyzes the performance of improved FCM algorithms for retinal image segmentation in terms of their ability and capability in segmenting and isolating blood vessels. The process we followed in our paper consists of two phases. Firstly, the pre-processing phase, where the green channel is taken for the color image of the retina. Contrast enhancement is performed through CLAHE , proceeded by applying bottom-hat filtering to bottom-hat filtering is applied with the purpose to define the region of interest. Secondly, in the segmentation phase the obtained image is segmented using FCM algorithms. The algorithms chosen for this study are: FCM, EnFCM, SFCM, FGFCM, FRFCM, DSFCM_N, FCM_SICM and, SSFCM performed on DRIVE and STARE databases. Experiments accomplished on DRIVE and STARE databases demonstrate that the DSFCM_N algorithm achieves better results on the DRIVE database, whereas the FGFCM algorithm provides better results on the STARE database in term of accuracy. Concerning time consumption. The FRFCM algorithm requires less time than other algorithms in the segmentation of retinal images.

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Generative Adversarial Network and Retinal Image Segmentation

Applications of Hybrid Metaheuristic Algorithms for Image Processing - Studies in Computational Intelligence ◽

10.1007/978-3-030-40977-7_21 ◽

2020 ◽

pp. 473-490

Author(s):

Talha Iqbal ◽

Hazrat Ali

Keyword(s):

Image Segmentation ◽

Retinal Image ◽

Generative Adversarial Network ◽

Adversarial Network ◽

Retinal Image Segmentation

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Reliability of Manual Vascular Segmentation for Retinal Fractal Dimension using Peek Retinatm

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.i3326.0789s319 ◽

2019 ◽

Vol 8 (9S3) ◽

pp. 1560-1564

Keyword(s):

Fractal Dimension ◽

Blood Vessel ◽

Blood Vessels ◽

Retinal Image ◽

Retinal Images ◽

Retinal Vasculature ◽

Manual Segmentation ◽

Good Reliability ◽

Fundus Camera ◽

Df Analysis

Fractal dimension (Df) has been identified as indirect measure in quantifying the complexity of retinal vessel network which is useful for early detection of vascular changes. Reliability studies of Df measurement on retinal vasculature, has been conducted on retinal images processed by using semi-automated software which only permits image with 45ᵒ field of view (FOV). Smartphone-assisted fundus camera retinal image has a maximum 30ᵒ FOV which warrant manual processing in measuring the Df. Retinal blood vessels need to be manually segmented to produce binary images for retinal vasculatures Df measurement. Therefore, this study was conducted to determine the intragrader and intergrader reliability of manual segmentation of the retinal vasculature Df measurement from retinal images taken using a smartphone-assisted fundus camera Forty-five retinal images were captured using the Portable Eye Examination Kit Retina (Peek Retina™, Peek Vision Ltd, UK). Suitable image for Df analysis were selected based on gradable retinal image criteria which included; i) good image focus, ii) centered position of optic nerve head (ONH) and iii) significant blood vessel visibility. The images were cropped 0.5 disc diameters away from disc margin and resized to 500x500 pixels using GNU Image Manipulation Program Version 2.8.18 (GIMP, The GIMP Team, United States). Retinal vessels were manually traced by using layering capabilities for blood vessel segmentation. Df values of segmented blood vessels were measured by using Image J (National Institutes of Health, USA) and its plugin software, FracLac Version 2.5. Intragrader and intergrader reliability was determined by comparing the Df values between; two readings measured one week apart by a grader and readings from two different graders, respectively, using intraclass correlation coefficient (ICC) and Bland-Altman graphical plots. Intragrader agreement for retinal Df showed good reliability with ICC of 0.899 (95% CI: 0.814–0.945). Bland Altman analysis indicated good agreement between Df values at different grading time (mean difference 0.0050; 95% CI:-0.0001–0.0101). Intergrader reliability for retinal Df was high with ICC of 0.814 (95% CI: 0.459–0.919). Bland Altman plot revealed good intergrader agreement for retinal Df between two graders with a bias value of 0.0158 (95% CI: 0.0092–0.0223). In conclusion, manual segmentation of retinal image captured by smartphone-assisted fundus camera has good reliability (0.75 < ICC < 0.9) for Df analysis to study the morphology of retinal vasculatures.

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