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    Detection of Optic Disc Localization from Retinal Fundus Image Using Optimized Color Space
    (Springer, 2022) Toptas, Buket; Toptas, Murat; Hanbay, Davut
    Optic disc localization offers an important clue in detecting other retinal components such as the macula, fovea, and retinal vessels. With the correct detection of this area, sudden vision loss caused by diseases such as age-related macular degeneration and diabetic retinopathy can be prevented. Therefore, there is an increase in computer-aided diagnosis systems in this field. In this paper, an automated method for detecting optic disc localization is proposed. In the proposed method, the fundus images are moved from RGB color space to a new color space by using an artificial bee colony algorithm. In the new color space, the localization of the optical disc is clearer than in the RGB color space. In this method, a matrix called the feature matrix is created. This matrix is obtained from the color pixel values of the image patches containing the optical disc and the image patches not containing the optical disc. Then, the conversion matrix is created. The initial values of this matrix are randomly determined. These two matrices are processed in the artificial bee colony algorithm. Ultimately, the conversion matrix becomes optimal and is applied over the original fundus images. Thus, the images are moved to the new color space. Thresholding is applied to these images, and the optic disc localization is obtained. The success rate of the proposed method has been tested on three general datasets. The accuracy success rate for the DRIVE, DRIONS, and MESSIDOR datasets, respectively, is 100%, 96.37%, and 94.42% for the proposed method.
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    A new artificial bee colony algorithm-based color space for fire/flame detection
    (Springer, 2020) Toptas, Buket; Hanbay, Davut
    Image processing-based fire/flame detection has become popular in recent years. In this paper, a novel fire/flame detection system based on a new conversion matrix and artificial bee colony algorithm was presented. Flame and non-flame image pixel values were combined to have a new feature matrix. A conversion matrix was generated randomly. The conversion matrix was multiplied by the feature matrix. The error of this multiplication result was calculated using theK-means clustering algorithm. The conversion matrix was updated until getting desired performance using artificial bee colony algorithm. At the end of the updating process, updated conversion matrix was multiplied with all images in the dataset to move all images to new color space. The final images were converted into binary images. Otsu method was used to get binary images. These binary images were compared with the corresponding ground truth images in the dataset. The aim of this comparison is to calculate the similarity ratio of the two images. This ratio shows the extent to which the original image features are preserved. A forest fire dataset was used which has 500 forest fire images. It is publicly available and called as Corsican Fire Database. Jaccard and Dice similarity measure parameters were used to evaluate the proposed system performance and compared with other similar study such as particle swarm optimization. Evaluated mean Jaccard index value was 0.76, and mean Dice index value was 0.85. This evaluation was made for 500 images. These results provide that this system can be used in fire/flame detection systems.
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    Performance Comparison of Different Optimization Algorithms
    (Ieee, 2018) Toptas, Buket; Karadeniz, Esra; Karci, Ali
    Optimization algorithms are popular approaches to solving problems in many field. By considering the performance criteria of the optimization algorithms, optimization algorithms suitable for the topic are selected. In this study, the performance criteria of the five optimization algorithms, which have the same mathematical test functions and the parameter values of these functions and the decision variables, the number of populations and the number of execution cycles of the algorithm, are compared. Artificial Bee Colony Algorithm, Particle Swarm Optimization Algorithm, Fire Beetle Algorithm, Symbiotic Organism Algorithm and Biogeography Based Optimization Algorithm are used as optimization algorithms. Performance measures of these five optimization algorithms are calculated on three different benchmarking algorithms. The calculation results are presented as numerical values.
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    Retinal blood vessel segmentation using pixel-based feature vector
    (Elsevier Sci Ltd, 2021) Toptas, Buket; Hanbay, Davut
    A lot of important disease information can be accessed by performing retinal blood vessel analysis on fundus images. Diabetic retinopathy is one of the diseases understood by retinal blood vessel analysis. If this disease is detected at an early stage, vision loss can be prevented. In this paper, a method that performs retinal blood vessel analysis with classical methods is proposed. In this proposed system, pixel-based feature extraction is performed. Five different feature groups are used for feature extraction. These feature groups are edge detection, morphological, statistical, gradient, and Hessian matrix. An 18-D feature vector is created for each pixel. This feature vector is given to the artificial neural network for training. Using test images, the system is tested on two publicly available datasets. Sensitivity, Specificity, and Accuracy performance measures were used as success measures. The similarity index between the segmented image and the ground truth is measure using Dice and Jaccard. The accuracy of the system was measured as 96.18% for DRIVE and 94.56% for STARE, respectively. Experimental results show that the proposed algorithm achieves satisfactory results. This method can be used as an automated retinal blood vessel segmenting system.
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    Separation of arteries and veins in retinal fundus images with a new CNN architecture
    (Taylor & Francis Ltd, 2023) Toptas, Buket; Hanbay, Davut
    Retinal blood vessels are directly or indirectly associated with many diseases. The retinal blood vessels consist of artery and vein vessels. With the automatic correct identification of these vessels, many diseases can be prevented. In this paper, a method is proposed to separate between arteries and veins on retinal blood vessel images. In the proposed method, firstly, the image preprocessing step is applied. Then, image patches are obtained from pre-processed retinal fundus images. These patches are prepared as input to the deep learning network architecture. The proposed deep learning network architecture is presented as a new CNN architecture. This architecture decides whether the blood vessel pixels in the fundus image are arteries or veins. The proposed method was evaluated on publicly available given DRIVE, INSPIRE datasets, and the recently created LES-AV dataset. The performance of the proposed method was evaluated by using the most commonly used sensitivity, specificity, and accuracy performance measures. The accuracy measure for all vessel pixels is 0.9110 for DRIVE, 0.9654 for INSPIRE, and 0.9531 for LES-AV dataset. The proposed method is compared with other state-of-the-art artery/vein separation methods. The experimental results of the proposed method are promising. This method is suitable for automatic artery/vein separation.
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    Separation of Fire Images with Biogeography-Based Optimization
    (Ieee, 2018) Toptas, Buket; Hanbay, Davut; Yeroglu, Celaleddin
    Usage of color information of object is one of the most popular image processing based object detection method. In this work, a method has been proposed for separating color pixels of target objects from other object color pixels in the image. A fire/flame dataset was used for the target colored object. The proposed method consists of three stage. In the first stage, the flame and non-flame color pixels, taken from this dataset, are combined in a feature matrix. In the second stage, the feature matrix is subjected to linear multiplication with a 3*3 conversion matrix whose initial values are randomly generated. The error values of the linear multiplication result are calculated by the K-means algorithm and then these error values are optimized by the Biogeography Based Optimization (BBO) algorithm. In the last stage, the optimal conversion matrix is applied on the images and the color distribution of the target color pixels in three dimensional space is examined.