Feature Extraction Method of Retinal Vessel Diameter

Hoque, Mohammed Enamul; Kipli, Kuryati; Zulcaffle, Tengku Mohd Afendi; Sapawi, Rohana; Joseph, Annie; Abidin, Wan Azlan Wan Zainal; Sahari, Siti Kudnie

doi:10.1109/iecbes.2018.8626660

Cited by 5 publications

(4 citation statements)

References 21 publications

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“…Lee et al proposed a robust line fitting algorithm 5 to detect the blood vessels for locating the geometric corners on the retinal images. Euclidean distance transform 6 is proposed to measure retinal vessel diameter using the Euclidean distance between the bright pixels which are present in the region of interest of the fundus image. The significant advantage of this method is that it can eliminate time consuming manual and subjective process.…”

Section: Related Workmentioning

confidence: 99%

Improvement of thin retinal vessel extraction using mean matting method

Sathananthavathi¹,

Indumathi²,

Mahiya³

et al. 2021

Int J Imaging Syst Tech

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In this paper, a new mean matting method based on mean correlation is proposed to extract thin blood vessels precisely. The proposed algorithm is the combination of both supervised and unsupervised method. The supervised methodology performs well in extracting thick blood vessels; however, thin vessels are not precisely extracted. Even the capability of unsupervised method is better in extracting thin vessels; it has some artifacts in the output. The proposed method combines the advantages of both supervised and unsupervised method to extract vessel regions more precisely irrespective of their thickness.Using supervised methodology, thick blood vessels are extracted by training the classifier with the significant features describing the vessel regions. Trimap is generated on the unsupervised output and mean correlation is computed for all unknown pixels in the trimap to classify those pixels into vessels or background. The proposed matting method has less computational complexity compared to other existing matting methods. The performance of the proposed method is evaluated in detail on DRIVE and STARE datasets.

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Section: Related Workmentioning

confidence: 99%

Improvement of thin retinal vessel extraction using mean matting method

Sathananthavathi¹,

Indumathi²,

Mahiya³

et al. 2021

Int J Imaging Syst Tech

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“…These risky cardiovascular diseases are related to the changes in the microvasculature of human retina [9]. According to the different researches, there is a close relationship between the ocular funduscopic abnormalities and acute stroke even-though the blood pressure and other vascular risk factors are stable [10], [11]. Any damage in retinal arterioles and venules cause the HR that can lead to blindness [12].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning in Retinal Image Segmentation and Feature Extraction: A Review

Hoque

Kipli

2021

Int. J. Onl. Eng.

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Image recognition and understanding is considered as a remarkable subfield of Artificial Intelligence (AI). In practice, retinal image data have high dimensionality leading to enormous size data. As the morphological retinal image datasets can be analyzed in an expansive and non-invasive way, AI more precisely Deep Learning (DL) methods are facilitating in developing intelligent retinal image analysis tools. The most recently developed DL technique, Convolutional Neural Network (CNN) showed remarkable efficiency in identifying, localizing, and quantifying the complex and hierarchical image features that are responsible for severe cardiovascular diseases. Different deep layered CNN architectures such as LeeNet, AlexNet, and ResNet have been developed exploiting CNN morphology. This wide variety of CNN structures can iteratively learn complex data structures of different datasets through supervised or unsupervised learning and perform exquisite analysis for feature recognition independently to diagnose threatening cardiovascular diseases. In modern ophthalmic practice, DL based automated methods are being used in retinopathy screening, grading, identifying, and quantifying the pathological features to employ further therapeutic approaches and offering a wide potentiality to get rid of ophthalmic system complexity. In this review, the recent advances of DL technologies in retinal image segmentation and feature extraction are extensively discussed. To accomplish this study the pertinent materials were extracted from different publicly available databases and online sources deploying the relevant keywords that includes retinal imaging, artificial intelligence, deep learning and retinal database. For the associated publications the reference lists of selected articles were further investigated.

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“…To train, test, and evaluate the performance of this proposed method, colour retinal images from different publicly available databases have been extracted. The development of this DL approach is aimed to integrate into our previous work the IP algorithm for retinal vessel diameter quantification of different interesting locations of retinal images for creating a fully automated vessel diameter quantification method (Hoque et al, 2019;Hoque et al, 2018;Kipli et al, 2020). The applied methodology, including training and testing for the development of this proposed DL method, is explained in detail in the following section.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Approach for Retinal Image Feature Extraction

Hoque¹,

Kipli²,

Zulcaffle³

et al. 2021

JST

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Retinal image analysis is crucially important to detect the different kinds of life-threatening cardiovascular and ophthalmic diseases as human retinal microvasculature exhibits remarkable abnormalities responding to these disorders. The high dimensionality and random accumulation of retinal images enlarge the data size, that creating complexity in managing and understating the retinal image data. Deep Learning (DL) has been introduced to deal with this big data challenge by developing intelligent tools. Convolutional Neural Network (CNN), a DL approach, has been designed to extract hierarchical image features with more abstraction. To assist the ophthalmologist in eye screening and ophthalmic disease diagnosis, CNN is being explored to create automatic systems for microvascular pattern analysis, feature extraction, and quantification of retinal images. Extraction of the true vessel of retinal microvasculature is significant for further analysis, such as vessel diameter and bifurcation angle quantification. This study proposes a retinal image feature, true vessel segments extraction approach exploiting the Faster RCNN. The fundamental Image Processing principles have been employed for pre-processing the retinal image data. A combined database assembling image data from different publicly available databases have been used to train, test, and evaluate this proposed method. This proposed method has obtained 92.81% sensitivity and 63.34 positive predictive value in extracting true vessel segments from the top first tier of colour retinal images. It is expected to integrate this method into ophthalmic diagnostic tools with further evaluation and validation by analysing the performance.

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Feature Extraction Method of Retinal Vessel Diameter

Cited by 5 publications

References 21 publications

Improvement of thin retinal vessel extraction using mean matting method

Improvement of thin retinal vessel extraction using mean matting method

Deep Learning in Retinal Image Segmentation and Feature Extraction: A Review

A Deep Learning Approach for Retinal Image Feature Extraction

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