Retinal blood vessel segmentation employing image processing and data mining techniques for computerized retinal image analysis

GeethaRamani, R.; Balasubramanian, Lakshmi

doi:10.1016/j.bbe.2015.06.004

Cited by 110 publications

(46 citation statements)

References 44 publications

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“…The performance of this study is very good on the specificity parameters, but the sensitivity is relatively low, so the AUC value is below 90%. Referring to research conducted by Budayan et al [3] confirmed that the ability of the SOM-ANN algorithm have a better ability than k-means so that the performance for the AUC parameters in the proposed research is better than that of Geetharamani and Balasubramanian [23].…”

Section: Methodsmentioning

confidence: 56%

“…The method used is a combination of lateral inhibition, differential evolution, and morphology. The research conducted by Geetharamani and Balasubramanian [23], the proposed method combines a number of methods, both in the process of preprocessing and segmentation. In the preprocessing process, the Gabor filter is used to repair it, while the segmentation process uses principle component analysis, k-means clustering, bagging classification, and image postprocessing.…”

Section: Methodsmentioning

confidence: 99%

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The Hybrid Method of SOM Artificial Neural Network and Median Thresholding for Segmentation of Blood Vessels in the Retina Image Fundus

Wiharto

2019

IJFIS

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Blood vessels in the retina of the eye are one important sign when making a diagnosis of hypertensive retinopathy. On the retina can be known several signs including tortuosity and arteriovenous ratio. Blood vessels mixed with a number of objects in the retina, the segmentation of blood vessels becomes a very interesting challenge because they have to separate blood vessels from a number of objects. This study aims to segmentation blood vessels using the main method of self-organizing maps artificial neural networks (SOM-ANN). The proposed segmentation method is divided into three stages, namely preprocessing, segmentation, and performance analysis. The preprocessing step is to improve image quality using the contrast-limited adaptive histogram equalization (CLAHE), median filter, and morphology. The segmentation stage uses the SOM-ANN algorithm combined with the mean or median thresholding. The performance parameters which are measured consist of sensitivity, specificity, and area under the curve (AUC). The test results using the dataset STARE and DRIVE show that the median thresholding is able to provide the best AUC performance compared to the mean thresholding. The proposed segmentation model is able to provide performance in the excellent category, with AUC values of 90.55% for the STARE dataset and 90.35% for the DRIVE.

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Section: Methodsmentioning

confidence: 56%

Section: Methodsmentioning

confidence: 99%

The Hybrid Method of SOM Artificial Neural Network and Median Thresholding for Segmentation of Blood Vessels in the Retina Image Fundus

Wiharto

2019

IJFIS

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“…A sensitivity (SE) of 96.1% and specificity (SP) of 92.2% in the classification of true vessel points was reported. A comprehensive review of supervised-related, unsupervised-related, image processing-related, and data miningrelated algorithms is presented in [45][46][47] with high vessel segmentation results. The study in [46] combined normal and abnormal retinal features with the patient's contextual information at adaptive granularity levels through data mining methods to segment retinal vessels.…”

Section: Blood Vessel Extractionmentioning

confidence: 99%

“…However, the system running time was highly expensive. Another study in [47] used the sequential application of image pre-/post-processing and data mining methods for vessel segmentation. Their framework reported an accuracy of 95.36% at the cost of a long processing time and low specificity (SP) for vessel segmentation.…”

Section: Blood Vessel Extractionmentioning

confidence: 99%

Recent Development on Detection Methods for the Diagnosis of Diabetic Retinopathy

Qureshi

Abbas

2019

Symmetry

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Diabetic retinopathy (DR) is a complication of diabetes that exists throughout the world. DR occurs due to a high ratio of glucose in the blood, which causes alterations in the retinal microvasculature. Without preemptive symptoms of DR, it leads to complete vision loss. However, early screening through computer-assisted diagnosis (CAD) tools and proper treatment have the ability to control the prevalence of DR. Manual inspection of morphological changes in retinal anatomic parts are tedious and challenging tasks. Therefore, many CAD systems were developed in the past to assist ophthalmologists for observing inter- and intra-variations. In this paper, a recent review of state-of-the-art CAD systems for diagnosis of DR is presented. We describe all those CAD systems that have been developed by various computational intelligence and image processing techniques. The limitations and future trends of current CAD systems are also described in detail to help researchers. Moreover, potential CAD systems are also compared in terms of statistical parameters to quantitatively evaluate them. The comparison results indicate that there is still a need for accurate development of CAD systems to assist in the clinical diagnosis of diabetic retinopathy.

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“…Compared to traditional techniques, AI provides greater flexibility when analyzing images. The survey presented in [13] demonstrates increased precision thanks to the evolution of AI techniques.…”

Section: Introductionmentioning

confidence: 99%

Information Extraction from Retinal Images with Agent-Based Technology

2018

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The study of retinal vessels can provide information on a wide range of illnesses in the human body. Numerous works have already focused on this new field of research and several medical software programs have been proposed to facilitate the close examination of retinal vessels. Some allow for the automatic extraction of information and can be combined with other clinical tools for effective diagnosis and further medical studies. This article proposes an Agent-based Virtual Organizations (VO) System which applies a novel methodology for taking measurements from fundus images and extracting information on the retinal vessel caliber. A case study was conducted to evaluate the performance of the developed system, and the fundus images of different patients were used to extract information. Its performance was compared with that of similar tools.

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Retinal blood vessel segmentation employing image processing and data mining techniques for computerized retinal image analysis

Cited by 110 publications

References 44 publications

The Hybrid Method of SOM Artificial Neural Network and Median Thresholding for Segmentation of Blood Vessels in the Retina Image Fundus

The Hybrid Method of SOM Artificial Neural Network and Median Thresholding for Segmentation of Blood Vessels in the Retina Image Fundus

Recent Development on Detection Methods for the Diagnosis of Diabetic Retinopathy

Information Extraction from Retinal Images with Agent-Based Technology

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