Blood vessel extraction from retinal images using Complex Wavelet Transform and Complex-Valued Artificial Neural Network

Ceylan, Murat; Yacar, Huseyin

doi:10.1109/tsp.2013.6614053

Cited by 13 publications

(14 citation statements)

References 6 publications

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“…Within the scope of the study, DT-CWT process was used with a scale (level) value of 1, and the dimensions of the sub-band images obtained were half the size of the original image. Since the complex wavelet transform has been successful in many studies [32]- [34] where medical images were used before, this conversion was preferred in the study.…”

Section: Dual Tree Complex Wavelet Transform (Dt-cwt)mentioning

confidence: 99%

A New Deep Learning Pipeline to Detect Covid-19 on Chest X-Ray Images using Local Binary Pattern, Dual Tree Complex Wavelet Transform and Convolutional Neural Networks

Yaşar

Ceylan

2020

Preprint

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At the end of 2019, a new type of virus, belonging to the coronaviridae family has emerged and it is considered that the virus in question is of zootonic origin. The virus that emerged in China first affected this country and then spread worldwide. Pneumonia develops due to Covid-19 virus in patients having severe disease symptoms. Many literature studies have been carried out in the process where the effects of the disease-induced pneumonia in lungs have been demonstrated with the help of chest X-ray imaging. In this study, which aims at early diagnosis of Covid-19 disease by using X-Ray images, the deep-learning approach, which is a state-of-the-art artificial intelligence method, was used and automatic classification of images was performed using Convolutional Neural Networks (CNN). In the first training-test data set used in the study, there were a total of 230 abnormal and 80 normal X-Ray images, while in the second training-test data set there were 476 X-Ray images, of which 150 abnormal and 326 normal. Thus, classification results have been provided for two data sets, containing predominantly abnormal images and predominantly normal images respectively. In the study, a 23-layer CNN architecture was developed. Within the scope of the study, results were obtained by using chest X-Ray images directly in training-test procedures and the sub-band images obtained by applying Dual Tree Complex Wavelet Transform (DT-CWT) to the above-mentioned images. The same experiments were repeated using images obtained by applying Local Binary Pattern (LBP) to the chest X-Ray images. Within the scope of the study, a new result generation algorithm having been put forward additionally, it was ensured that the experimental results were combined and the success of the study was improved. In the experiments carried out in the study, the trainings were carried out using the k-fold cross validation method. Here the k value was chosen 23. Considering the highest results of the tests performed in the study, values of sensitivity, specificity, accuracy and AUC for the first training-test data set were calculated to be 1, 1, 0,9913 and 0,9996; while for the second data set of training-test, they were 1, 0,9969, 0,9958 and 0,9996 respectively. Considering the average highest results of the experiments performed within the scope of the study, the values of sensitivity, specificity, accuracy and AUC for the first training-test data set were 0,9933, 0,9725, 0,9843 and 0,9988; while for the second training-test data set, they were 0,9813, 0,9908, 0,9857 and 0,9983 respectively.

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Section: Dual Tree Complex Wavelet Transform (Dt-cwt)mentioning

confidence: 99%

A New Deep Learning Pipeline to Detect Covid-19 on Chest X-Ray Images using Local Binary Pattern, Dual Tree Complex Wavelet Transform and Convolutional Neural Networks

Yaşar

Ceylan

2020

Preprint

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“…The accuracy rate obtained using Bayes classifier was 0,9614 [8]. In another study, Ceylan et al achieved the accuracy of 98.56% by using complex wavelet transform and complex artificial neural network [9]. Vega et al performed segmentation using a lattice neural network with dendritic processing and obtained 0,9412 accuracy for the DRIVE data set and 0,9483 accuracy for the STARE data set [10].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Unsupervised Segmentation of Retinal Blood Vessels in Gray Level Image with PCA and Green Channel Image

Kaya

Sarıtaş

Ceylan

2017

ijisae

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Abstract:In this study, an unsupervised retina blood vessel segmentation process was performed on the gray level images with principal component analysis (PCA) and the green channel of the RGB image, which most clearly shows retinal vessels and the results were compared. The images were improved for a good segmentation by using contrast-limited adaptive histogram equalization (CLAHE), color intensity adjustment, morphological operations and median and Gaussian filtering. Retinal vessel structures were segmented with top-hat and bot-hat morphological operations and converted to binary image by using global thresholding. The average accuracy rate obtained for the gray level image with PCA after the study was 0.9443, while the average accuracy rate obtained for the green channel was 0.9685. The study was performed using 40 images in the DRIVE data set which is one of the most common retina data sets known.

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“…They used transfer learning of tissue-specific photon interaction statistical physics and obtained an average accuracy of 97.66%. Ceylan and Yaşar [42] proposed complex wavelet transform and a complex valued artificial neural network for retinal image segmentation. To test the methods, the DRIVE database was used and the accuracy value was calculated as 98.56%.…”

Section: Introductionmentioning

confidence: 99%

A novel approach for automatic blood vessel extraction in retinal images: complex ripplet-I transform and complex valued artificial neural network

Ceylan¹,

Yaşar²

2016

Turk J Elec Eng & Comp Sci

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This study determined the features of line, curve, and ridge structures in images using complex ripplet-I and enabled extraction of blood vessel networks from retinal images through a complex valued artificial neural network using those features. Forty color fundus images in the DRIVE database and 20 color fundus images in the STARE database were used to test the success of the proposed system. In this study, a complex version of ripplet-I transform was used for the first time. By presenting the directed image for the determination of the unique geometrical properties of the vessel regions, complex ripplet-I transforms showing better performance than other types of multiresolution analysis were combined with a complex valued ANN. The results in the study were reobtained using leave-one-out crossvalidation method with bagging technique in order to ensure the stability and correctness of the performance. In the DRIVE database, the highest average accuracy of the system was found to be 98.44% for complex ripplet-I transform and complex valued ANN. For the STARE database (labeled by Adam Hoover), highest average accuracy rates were obtained as 99.25% for complex ripplet-I transforms and complex valued ANN. Similarly, for the other labeled data (by Valentina Kouznetsova), highest average accuracy rates were obtained as 98.03% for complex ripplet-I transforms and complex valued ANN.

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Blood vessel extraction from retinal images using Complex Wavelet Transform and Complex-Valued Artificial Neural Network

Cited by 13 publications

References 6 publications

A New Deep Learning Pipeline to Detect Covid-19 on Chest X-Ray Images using Local Binary Pattern, Dual Tree Complex Wavelet Transform and Convolutional Neural Networks

A New Deep Learning Pipeline to Detect Covid-19 on Chest X-Ray Images using Local Binary Pattern, Dual Tree Complex Wavelet Transform and Convolutional Neural Networks

Comparison of Unsupervised Segmentation of Retinal Blood Vessels in Gray Level Image with PCA and Green Channel Image

A novel approach for automatic blood vessel extraction in retinal images: complex ripplet-I transform and complex valued artificial neural network

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