The Analysis Effect of Cluster Numbers On Fuzzy C-Means Algorithm for Blood Vessel Segmentation of Retinal Fundus Image

Wiharto, Wiharto; Suryani, Esti

doi:10.1109/icoiact46704.2019.8938583

Cited by 14 publications

(10 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The next step is to obtain the image of the blood vessels by thresholding the contrast stretched image. The thresholding process is carried out using the median centroid value of the cluster generated in the SOM process [3], [5]. The result of thresholding is a binary image.…”

Section: -3-segmentationmentioning

confidence: 99%

“…In addition to the number of clusters 2, the performance of CAD-RH, in the number of clusters of 5, is also able to provide performance with AUC values> 80%. The advantage of the number of clusters 5 is that it only requires 3 features, namely the fractal dimensions, the lacunarity with the size box are 2 1 and 2 3 . The weakness of the number of clusters 5 is that the specificity value has a large difference compared to the number of clusters 2.…”

Section: Output Thresholdingmentioning

confidence: 99%

“…Segmentation has several approaches, one of which is clustering [2]. Retinal image segmentation has been done a lot, as done by Wiharto et al [3]. This study analyzes the effect of the number of clusters on segmentation performance using the fuzzy c-means clustering algorithm.…”

Section: -Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Performance Analysis of Hybrid SOM and AdaBoost Classifiers for Diagnosis of Hypertensive Retinopathy

Wiharto¹,

Suryani²,

Susilo³

2021

JIST

Self Cite

View full text Add to dashboard Cite

The diagnosis of hypertensive retinopathy (CAD-RH) can be made by observing the tortuosity of the retinal vessels. Tortuosity is a feature that is able to show the characteristics of normal or abnormal blood vessels. This study aims to analyze the performance of the CAD-RH system based on feature extraction tortuosity of retinal blood vessels. This study uses a segmentation method based on clustering self-organizing maps (SOM) combined with feature extraction, feature selection, and the ensemble Adaptive Boosting (AdaBoost) classification algorithm. Feature extraction was performed using fractal analysis with the box-counting method, lacunarity with the gliding box method, and invariant moment. Feature selection is done by using the information gain method, to rank all the features that are produced, furthermore, it is selected by referring to the gain value. The best system performance is generated in the number of clusters 2 with fractal dimension, lacunarity with box size 2 2 -2 9 , and invariant moment M 1 and M 3 . Performance in these conditions is able to provide 84% sensitivity, 88% specificity, 7.0 likelihood ratio positive (LR+), and 86% area under the curve (AUC). This model is also better than a number of ensemble algorithms, such as bagging and random forest. Referring to these results, it can be concluded that the use of this model can be an alternative to CAD-RH, where the resulting performance is in a good category.

show abstract

Section: -3-segmentationmentioning

confidence: 99%

Section: Output Thresholdingmentioning

confidence: 99%

See 1 more Smart Citation

Performance Analysis of Hybrid SOM and AdaBoost Classifiers for Diagnosis of Hypertensive Retinopathy

Wiharto¹,

Suryani²,

Susilo³

2021

JIST

Self Cite

View full text Add to dashboard Cite

show abstract

“…The literature consists of some research into the segmentation of images in medical research such as the fuzzy clustering method with residual driving and automatic fuzzy clustering methods [22,23]. Algorithms that are without 2 BioMed Research International supervision do not involve training networks; they are called clustering methods, e.g., Fast FCM, and Robust FCM [24][25][26][27][28][29].…”

Section: Introductionmentioning

confidence: 99%

“…Methods such as FCM [ 21 , 35 ], KNN [ 21 , 36 ], and SVM [ 37 ] are commonly used as automated methods for generating ground truth data for automated methods. The advantage of automated methods like FCM is the detection of locations with distinguished color [ 29 , 38 ]. In other words, tumors should be light or dark and different from other pixels.…”

Section: Introductionmentioning

confidence: 99%

An Extended Approach to Predict Retinopathy in Diabetic Patients Using the Genetic Algorithm and Fuzzy C-Means

Ghoushchi

Ranjbarzadeh

Dadkhah

et al. 2021

BioMed Research International

View full text Add to dashboard Cite

The present study is developed a new approach using a computer diagnostic method to diagnosing diabetic diseases with the use of fluorescein images. In doing so, this study presented the growth region algorithm for the aim of diagnosing diabetes, considering the angiography images of the patients’ eyes. In addition, this study integrated two methods, including fuzzy C-means (FCM) and genetic algorithm (GA) to predict the retinopathy in diabetic patients from angiography images. The developed algorithm was applied to a total of 224 images of patients’ retinopathy eyes. As clearly confirmed by the obtained results, the GA-FCM method outperformed the hand method regarding the selection of initial points. The proposed method showed 0.78 sensitivity. The comparison of the fuzzy fitness function in GA with other techniques revealed that the approach introduced in this study is more applicable to the Jaccard index since it could offer the lowest Jaccard distance and, at the same time, the highest Jaccard values. The results of the analysis demonstrated that the proposed method was efficient and effective to predict the retinopathy in diabetic patients from angiography images.

show abstract

Evolutionary-based neuro-fuzzy modelling of combustion enthalpy of municipal solid waste

Adeleke

Akinlabi

Jen

et al. 2022

Neural Comput & Applic

View full text Add to dashboard Cite

The Analysis Effect of Cluster Numbers On Fuzzy C-Means Algorithm for Blood Vessel Segmentation of Retinal Fundus Image

Cited by 14 publications

References 13 publications

Performance Analysis of Hybrid SOM and AdaBoost Classifiers for Diagnosis of Hypertensive Retinopathy

Performance Analysis of Hybrid SOM and AdaBoost Classifiers for Diagnosis of Hypertensive Retinopathy

An Extended Approach to Predict Retinopathy in Diabetic Patients Using the Genetic Algorithm and Fuzzy C-Means

Evolutionary-based neuro-fuzzy modelling of combustion enthalpy of municipal solid waste

Contact Info

Product

Resources

About