Yazar "Abdullah, Ahmad S." seçeneğine göre listele

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    A new and effective method for human retina optic disc segmentation with fuzzy clustering method based on active contour model
    (Springer Heidelberg, 2020) Abdullah, Ahmad S.; Rahebi, Javad; Özok, Yasa Ekşioğlu; Aljanabi, Mohanad
    In this paper, a new approach is proposed for localization and segmentation of the optic disc in human retina images. This new approach can find the boundary of the optic disc by an initial fuzzy clustering means algorithm. The proposed approach uses active contour model evolution based on a fuzzy clustering algorithm. The robustness of the proposed approach was evaluated with retinal imaging medical databases, such as DRIVE, STARE, DIARETDB1, and DRIONS. These bases contained images affected by different abnormalities, for example, diabetes, retinitis pigmentosa, and age-related macular degeneration AMD. A success detection rate with 100% accuracy was achieved for the DRIVE, DIRATEDB1, and DRIONS-DB databases, and 97.53% for the STARE database. For the optic disc segmentation, the method achieved an average accuracy and overlap in the range of 97.01-99.46% and 78.35-84.56% in these four databases. The result was compared with various methods in the literature, and it was concluded that the proposed method is more accurate than the other existing methods.
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    A novel method for retinal optic disc detection using bat meta-heuristic algorithm
    (Springer Heidelberg, 2018) Abdullah, Ahmad S.; Özok, Yasa Ekşioğlu; Rahebi, Javad
    Normally, the optic disc detection of retinal images is useful during the treatment of glaucoma and diabetic retinopathy. In this paper, the novel preprocessing of a retinal image with a bat algorithm (BA) optimization is proposed to detect the optic disc of the retinal image. As the optic disk is a bright area and the vessels that emerge from it are dark, these facts lead to the selected segments being regions with a great diversity of intensity, which does not usually happen in pathological regions. First, in the preprocessing stage, the image is fully converted into a gray image using a gray scale conversion, and then morphological operations are implemented in order to remove dark elements such as blood vessels, from the images. In the next stage, a bat algorithm (BA) is used to find the optimum threshold value for the optic disc location. In order to improve the accuracy and to obtain the best result for the segmented optic disc, the ellipse fitting approach was used in the last stage to enhance and smooth the segmented optic disc boundary region. The ellipse fitting is carried out using the least square distance approach. The efficiency of the proposed method was tested on six publicly available datasets, MESSIDOR, DRIVE, DIARETDB1, DIARETDB0, STARE, and DRIONS-DB. The optic disc segmentation average overlaps and accuracy was in the range of 78.5-88.2% and 96.6-99.91% in these six databases. The optic disk of the retinal images was segmented in less than 2.1s per image. The use of the proposed method improved the optic disc segmentation results for healthy and pathological retinal images in a low computation time.
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    Öğe
    A new and effective method for human retina optic disc segmentation with fuzzy clustering method based on active contour model (December, 10.1007/S11517-019-02032-8, 2019)
    (Springer Heidelberg, 2020) Abdullah, Ahmad S.; Rahebi, Javad; Ozok, Yasa Eksioglu; Aljanabi, Mohanad
    [No abstract available]
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    Skin lesion segmentation method for dermoscopy images using artificial bee colony algorithm
    (Mdpi, 2018) Aljanabi, Mohanad; Özok, Yasa Ekşioğlu; Rahebi, Javad; Abdullah, Ahmad S.
    The occurrence rates of melanoma are rising rapidly, which are resulting in higher death rates. However, if the melanoma is diagnosed in Phase I, the survival rates increase. The segmentation of the melanoma is one of the largest tasks to undertake and achieve when considering both beneath and over the segmentation. In this work, a new approach based on the artificial bee colony (ABC) algorithm is proposed for the detection of melanoma from digital images. This method is simple, fast, flexible, and requires fewer parameters compared with other algorithms. The proposed approach is applied on the PH2, ISBI 2016 challenge, the ISBI 2017 challenge, and Dermis datasets. These bases contained images are affected by different abnormalities. The formation of the databases consists of images collected from different sources; they are bases with different types of resolution, lighting, etc., so in the first step, the noise was removed from the images by using morphological filtering. In the next step, the ABC algorithm is used to find the optimum threshold value for the melanoma detection. The proposed approach achieved good results in the conditions of high specificity. The experimental results suggest that the proposed method accomplished higher performance compared to the ground truth images supported by a Dermatologist. For the melanoma detection, the method achieved an average accuracy and Jaccard's coefficient in the range of 95.24-97.61%, and 83.56-85.25% in these four databases. To show the robustness of this work, the results were compared to existing methods in the literature for melanoma detection. High values for estimation performance confirmed that the proposed melanoma detection is better than other algorithms, which demonstrates the highly differential power of the newly introduced features.