Retinal Blood Vessel Extraction using Wavelet Decomposition

Susetianingtias, Diana Tri; Madenda, Sarifuddin; Fitrianingsih,; Adlina, Dea; Rodiah,; Arianty, Rini

doi:10.14569/ijacsa.2020.0110448

Cited by 2 publications

(2 citation statements)

References 11 publications

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“…The results of the median filter operation are then continued by doing binarization [23] by determining the threshold value T which is the pixel mapping value that separates two pixels 0 (white) and 1 (black). Any pixel (x, y) that satisfies f (x, y)> T is the object pixel, that is, the veins and other pixels are the backgrounds [24]. The binarization result then is removed from the background using the first derivative of Gaussian [20] to obtain a segmented vascular candidate as shown in Fig.…”

Section: Retinal Blood Vessels Extractionmentioning

confidence: 99%

Biometric System for Person Authentication Using Retinal Vascular Branching Pattern

Susetianingtias,

Madenda,

Rodiah

et al. 2023

Jurnal Ilmu Komputer dan Informasi

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The person’s retina has its uniqueness that can be used as biometric recognition. The use of the retina as a marking feature in biometrics is more accurate in making calls, verification, and authentication. Retinal biometric characteristics are unique and difficult to manipulate, thus making the retinal biometric system one of the most reliable biometrics compared to other biometric characteristics. The retinal biometric system can be formed using extracted retinal vessels. The difficulty in extracting retinal vessels is a characteristic of retinal vessels. itself includes (central artery, central branch artery, central vein and central branch vein), the ratio of the thickness ratio between the different retinal arteries and veins (2:3), the location of the retinal artery and vein and the color. This complexity often results in errors in the retinal blood vessel extraction process, where not all blood vessel objects can be extracted properly which can reduce the accuracy of the retinal biometric system. This study will address the problem of extracting retinal vessels by proposing the use of an extraction method to produce truly unique retinal features to be included in the retinal biometric system by tracing all branches of the retinal vessels (consisting of: bifurcation, trifurcation and crossover). ). The accuracy results show that 99.81% of the images were correctly detected. The blood pattern is obtained by doing extraction which includes the preprocessing stage and is continued by doing the blood extraction stage. This pattern extraction result is used as a unique pattern to be included in the feature vector of the biometric system in identifying person based on the retina.

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Section: Retinal Blood Vessels Extractionmentioning

confidence: 99%

Biometric System for Person Authentication Using Retinal Vascular Branching Pattern

Susetianingtias,

Madenda,

Rodiah

et al. 2023

Jurnal Ilmu Komputer dan Informasi

View full text Add to dashboard Cite

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“…The initial stage is done by changing the image of the RGB into a green channel image. The selection of green channel as an image that will be used in image processing is because the image on the green channel has an intensity value that is neither too bright and nor too dark [19]. The result of green channel extraction then goes through incomplement process [20] which aims to invert the pixel intensity value in the image.…”

Section: Fundus Retina Image Preprocessingmentioning

confidence: 99%

Retinal biometric identification using convolutional neural network

Rodiah¹,

Madenda

Susetianingtias³

et al. 2021

Computer Optics

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Authentication is needed to enhance and protect the system from vulnerabilities or weaknesses of the system. There are still many weaknesses in the use of traditional authentication methods such as PINs or passwords, such as being hacked. New methods such as system biometrics are used to deal with this problem. Biometric characteristics using retinal identification are unique and difficult to manipulate compared to other biometric characteristics such as iris or fingerprints because they are located behind the human eye thus they are difficult to reach by normal human vision. This study uses the characteristics of the retinal fundus image blood vessels that have been segmented for its features. The dataset used is sourced from the DRIVE dataset. The preprocessing stage is used to extract its features to produce an image of retinal blood vessel segmentation. The image resulting from the segmentation is carried out with a two-dimensional image transformation such as the process of rotation, enlargement, shifting, cutting, and reversing to increase the quantity of the sample of the retinal blood vessel segmentation image. The results of the image transformation resulted in 189 images divided with the details of the ratio of 80 % or 151 images as training data and 20 % or 38 images as validation data. The process of forming this research model uses the Convolutional Neural Network method. The model built during the training consists of 10 iterations and produces a model accuracy value of 98 %. The results of the model's accuracy value are used for the process of identifying individual retinas in the retinal biometric system.

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Retinal Blood Vessel Extraction using Wavelet Decomposition

Cited by 2 publications

References 11 publications

Biometric System for Person Authentication Using Retinal Vascular Branching Pattern

Biometric System for Person Authentication Using Retinal Vascular Branching Pattern

Retinal biometric identification using convolutional neural network

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