13 results on '"Çapar, Abdulkerim"'
Search Results
2. Metaphase finding with deep convolutional neural networks
- Author
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Moazzen, Yaser, Çapar, Abdulkerim, Albayrak, Abdulkadir, Çalık, Nurullah, and Töreyin, Behçet Uğur
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- 2019
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3. Combined segmentation and classification-based approach to automated analysis of biomedical signals obtained from calcium imaging.
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Dursun, Gizem, Bijelić, Dunja, Ayşit, Neşe, Kurt Vatandaşlar, Burcu, Radenović, Lidija, Çapar, Abdulkerim, Kerman, Bilal Ersen, Andjus, Pavle R., Korenić, Andrej, and Özkaya, Ufuk
- Subjects
AMYOTROPHIC lateral sclerosis ,CALCIUM ,MEDICAL screening ,IMAGE analysis - Abstract
Automated screening systems in conjunction with machine learning-based methods are becoming an essential part of the healthcare systems for assisting in disease diagnosis. Moreover, manually annotating data and hand-crafting features for training purposes are impractical and time-consuming. We propose a segmentation and classification-based approach for assembling an automated screening system for the analysis of calcium imaging. The method was developed and verified using the effects of disease IgGs (from Amyotrophic Lateral Sclerosis patients) on calcium (Ca
2+ ) homeostasis. From 33 imaging videos we analyzed, 21 belonged to the disease and 12 to the control experimental groups. The method consists of three main steps: projection, segmentation, and classification. The entire Ca2+ time-lapse image recordings (videos) were projected into a single image using different projection methods. Segmentation was performed by using a multi-level thresholding (MLT) step and the Regions of Interest (ROIs) that encompassed cell somas were detected. A mean value of the pixels within these boundaries was collected at each time point to obtain the Ca2+ traces (time-series). Finally, a new matrix called feature image was generated from those traces and used for assessing the classification accuracy of various classifiers (control vs. disease). The mean value of the segmentation F-score for all the data was above 0.80 throughout the tested threshold levels for all projection methods, namely maximum intensity, standard deviation, and standard deviation with linear scaling projection. Although the classification accuracy reached up to 90.14%, interestingly, we observed that achieving better scores in segmentation results did not necessarily correspond to an increase in classification performance. Our method takes the advantage of the multi-level thresholding and of a classification procedure based on the feature images, thus it does not have to rely on hand-crafted training parameters of each event. It thus provides a semi-autonomous tool for assessing segmentation parameters which allows for the best classification accuracy. [ABSTRACT FROM AUTHOR]- Published
- 2023
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4. A multi-spectral myelin annotation tool for machine learning based myelin quantification [version 1; peer review: 1 not approved]
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Çapar, Abdulkerim, Çimen Yetiş, Sibel, Aladağ, Zeynep, Ekinci, Dursun Ali, Ayten, Umut Engin, Kerman, Bilal Ersen, and Töreyin, Behçet Uğur
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Machine Learning ,nervous system ,Fluorescence Images ,Myelin Quantification ,Image Analysis ,Myelin Annotation Tool - Abstract
Myelin is an essential component of the nervous system and myelin damage causes demyelination diseases. Myelin is a sheet of oligodendrocyte membrane wrapped around the neuronal axon. In the fluorescent images, experts manually identify myelin by colocalization of oligodendrocyte and axonal membranes that fit certain shape and size criteria. Because myelin wriggles along x-y-z axes, machine learning is ideal for its segmentation. However, machinelearning methods, especially convolutional neural networks (CNNs), require a high number of annotated images, which necessitates expert labor. To facilitate myelin annotation, we developed a workflow and a software for myelin ground truth extraction from multi-spectral fluorescent images. Additionally, we shared a set of myelin ground truths annotated using this workflow.
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- 2021
5. Gradient-based shape descriptors
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Çapar, Abdulkerim, Kurt, Binnur, and Gökmen, Muhittin
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- 2009
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6. Can Artificial Intelligence Help Cervical Cytopathologist to Detect High-Grade Squamous Intraepithelial Lesions on the Atrophic Background?
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TÜRKMEN, İLKNUR, DURSUN, GİZEM, ÖZKAYA, UFUK, ÇAPAR, ABDULKERİM, MÜEZZİNOĞLU, BAHAR, and TÖREYİN, BEHÇET UĞUR
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- 2020
7. DEVELOPMENT OF AUTOMATED ANALYSIS OF BIOMEDICALSIGNALS OBTAINED FROM CALCIUM IMAGING
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Radenovic, Lidija, Pavle R, Andjus, Korenic, Andrej, ÇAPAR, ABDULKERİM, DURSUN, GİZEM, ÖZKAYA, UFUK, KERMAN, BİLAL ERSEN, and Dunja, Bijelic
- Published
- 2019
8. Contribution of digital pathology on the determination of liver steatosis ratio Karaciǧer Yaǧlanma Orani Tespitinde Dijital Patolojinin Katkisi
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Çakir, Asli, Türkmen, Ilknur Çetinaslan, Saka, Burcu, Akhan, Asli Ünlü, Çapar, Abdulkerim, CEYRAN, Bahar, Çoban, GANİME, DOGUSOY, Gülen Bülbül, DURSUN, Nevra, ERHAN, Selma Şengiz, Gücin, ZÜHAL, KAHRAMAN, Zehra Sibel, KAMALI, Gülçin, KEPIL, Nuray, KESER, Sevinç Hallaç, KIRIMLIOGLU, Hale, ÖZGÜVEN, Banu Yilmaz, ÖZKAN, Yasemin, PAŞAOĞLU, Esra, TUNÇEL, Deniz, YILMAZ, Müberra Seǧmen, and GÜCİN, ZÜHAL
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Çakir A., Türkmen I. Ç. , Saka B., Akhan A. Ü. , Çapar A., CEYRAN B., Çoban G., DOGUSOY G. B. , DURSUN N., ERHAN S. Ş. , et al., -Contribution of digital pathology on the determination of liver steatosis ratio Karaciǧer Yaǧlanma Orani Tespitinde Dijital Patolojinin Katkisi-, Gazi Medical Journal, cilt.29, ss.179-182, 2018 - Published
- 2018
9. Object segmentation and recognition using gradient based descriptors and shape driven fast marching methods
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Çapar, Abdulkerim, Gökmen, Muhittin, and Bilgisayar Mühendisliği Ana Bilim Dalı
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Image processing algorithms ,Computer vision ,Computer Engineering and Computer Science and Control ,Bilgisayar Mühendisliği Bilimleri-Bilgisayar ve Kontrol ,Image analysis - Abstract
Bu çalışmada, aktif çevrit nesne bölütleyici yöntemlerle birlikte kullanılabilecek yeni bir şekil betimleme ve tanıma sistemi önerilmiştir. Önerilen sistem daha önce yapılan çalışmalar gibi aktif çevriti önceden tanımlı şekillerden birine zorlamak yerine, çevrit nesne sınırlarına yapışırken aynı zamanda şekil betimleme yapmayı amaçlamıştır.Aktif çevrit bölütleyici olarak Hızlı Yürüme (Fast Marching) algoritması kullanılmış, Hızlı Yürüme metodu için yeni bir hız işlevi tanımlanmıştır. Ayrıca çevriti nesne sınırlarından geçtiği sırada durdurmayı amaçlayan özgün yaklaşımlar önerilmiştir.Çalışmanın en önemli katkılarından birisi yeni ortaya atılan Gradyan Temelli Şekil Betimleyicisi (GTŞB) dir [1]. GTŞB, aktif çevrit bölütleyicilerin yapısına uygun, sınır tabanlı, hem ikili hem de gri-seviyeli görüntülerle rahatça kullanılabilecek başarılı bir şekil betimleyicidir. GTŞB nin araç plaka karakter veritabanı, MPEG-7 şekil veritabanı, Kimia şekil veritabanı gibi farklı şekil veritabanlarında elde ettiği başarılar diğer çok bilinen sınır tabanlı betimleyicilerle de karşılaştırılarak verilmiştir. Elde edilen sonuçlar GTŞB nin tüm veritabanlarında diğer yöntemlere göre daha başarılı olduğunu işaret etmektedir.Çalışmada geliştirilen bir diğer önemli yaklaşım da Hızlı Yürüme çevritinin nesne sınırına yaklaşırken örneklenerek şeklin birden fazla defa betimlenmesine olanak veren yeni sınıflandırıcı yapıdır. Bu yaklaşım nesne tanımayı bir denemede sonuçlandıran geleneksel yöntemlerin bu sınırlamasını aşarak aynı nesneyi birçok kez tanıma olanağı sunmaktadır. Bu tanıma sonuçlarının tümleştirilmesiyle tek tanımaya göre daha yüksek başarılar elde edildiği çalışmanın ilgili bölümlerinde başarıları karşılaştıran tablolar yardımıyla gösterilmektedir. In this thesis, a gradient based shape description and recognition methodology to use with active contour-based object segmentation systems has been proposed.The Fast Marching (FM) active contour evolving model is utilized for boundary segmentation. A new speed functional has been defined to use first and second order image intensity derivatives. A local front stopping algorithm has also been proposed to improve the boundary handling performance of the FM model.The most critical improvement of the thesis is defining a new shape descriptor called the Gradient Based Shape Descriptor (GBSD) [1]. GBSD is a new boundary-based shape descriptor that can operate on both binary and gray-scaled images. The recognition performance of GBSD is measured on a license plate character database, MPEG-7 Core Experiments shape data set and Kimia data Set. The success rates are compared with other well-known boundary-based shape descriptors and it is shown that GBSD achieves better recognition percentages.A new recognition approach that utilizes the progressive active contours while iterating towards the real object boundaries has been proposed. This approach provides the recognizer many trials for shape description; it removes the limitation of traditional recognition systems that have only one chance for shape classification. Test results shown in this study prove that the voted decision result among these iterated contours outperforms the ordinary individual shape recognizers. 94
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- 2010
10. Affine Invariant Gradient Based Shape Descriptor.
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Gunsel, Bilge, Jain, Anil K., Tekalp, A. Murat, Sankur, Bülent, Çapar, Abdulkerim, Kurt, Binnur, and Gökmen, Muhittin
- Abstract
This paper presents an affine invariant shape descriptor which could be applied to both binary and gray-level images. The proposed algorithm uses gradient based features which are extracted along the object boundaries. We use two-dimensional steerable G-Filters [1] to obtain gradient information at different orientations. We aggregate the gradients into a shape signature. The signatures derived from rotated objects are shifted versions of the signatures derived from the original object. The shape descriptor is defined as the Fourier transform of the signature. We also provide a distance definition for the proposed descriptor taking shifted property of the signature into account. The performance of the proposed descriptor is evaluated over a database containing license plate characters. The experiments show that the devised method outperforms other well-known Fourier-based shape descriptors such as centroid distance and boundary curvature. [ABSTRACT FROM AUTHOR]
- Published
- 2006
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- View/download PDF
11. Myelin detection in fluorescence microscopy images using machine learning.
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Çimen Yetiş, Sibel, Çapar, Abdulkerim, Ekinci, Dursun A., Ayten, Umut E., Kerman, Bilal E., and Töreyin, B. Uğur
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SUPERVISED learning , *MYELIN , *FLUORESCENCE microscopy , *MACHINE learning , *NEUROGLIA - Abstract
• Myelin damage is at the heart of many neurodegenerative diseases such as MS. • Drug discovery for MS requires manual myelin counting on the microscopic images. • Myelin detection may be expedited by orders of magnitude using machine learning. • Myelin detection performances of 23 machine learning techniques were evaluated. • Boosted Trees and customized-CNN were accurate (over 98%), robust, and fast. The myelin sheath produced by glial cells insulates the axons, and supports the function of the nervous system. Myelin sheath degeneration causes neurodegenerative disorders, such as multiple sclerosis (MS). There are no therapies for MS that promote remyelination. Drug discovery frequently involves screening thousands of compounds. However, this is not feasible for remyelination drugs, since myelin quantification is a manual labor-intensive endeavor. Therefore, the development of assistive software for expedited myelin detection is instrumental for MS drug discovery by enabling high-content image-based drug screens. In this study, we developed a machine learning based expedited myelin detection approach in fluorescence microscopy images. Multi-channel three-dimensional microscopy images of a mouse stem cell-based myelination assay were labeled by experts. A spectro-spatial feature extraction method was introduced to represent local dependencies of voxels both in spatial and spectral domains. Feature extraction yielded two data set of over forty-seven thousand annotated images in total. Myelin detection performances of 23 different supervised machine learning techniques including a customized-convolutional neural network (CNN), were assessed using various train/test split ratios of the data sets. The highest accuracy values of 98.84 ± 0.09 % and 98.46 ± 0.11 % were achieved by Boosted Trees and customized-CNN, respectively. Our approach can detect myelin in a common experimental setup. Myelin extending in any orientation in 3 dimensions is segmented from 3 channel z-stack fluorescence images. Our results suggest that the proposed expedited myelin detection approach is a feasible and robust method for remyelination drug screening. [ABSTRACT FROM AUTHOR]
- Published
- 2020
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12. Oligodendrocyte interactome in healthy and diseased nervous system.
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Kerman, Bilal Ersen, Aydınlı, Fatmagül İlayda, Vatandaşlar, Burcu Kurt, Yurduseven, Kübra, Vatandaşlar, Emre, Çelik, Eşref, Yetiş, Sibel Çimen, Çapar, Abdulkerim, Aladağ, Zeynep, Ekinci, Dursun Ali, Ayten, Umut Engin, Töreyin, Behçet Uğur, and Kurnaz, Işıl Aksan
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NEUROLOGICAL disorders ,OLIGODENDROGLIA ,MYELIN proteins ,NERVOUS system ,DEMYELINATION ,MYELIN ,MULTIPLE sclerosis - Abstract
Objective: Myelin is essential for a healthy nervous system. Myelin formed by oligodendrocytes, accelerates impulse propagation and supports neuronal survival. Demyelination leads to neurodegeneration. In multiple sclerosis (MS) immune attack results in demyelination. Our goal is to dissect interactions among oligodendrocytes, neurons, and immune cells to improve our understanding of myelination and the demyelinating diseases. We aim to discover new targets for remyelination therapies. Methods: We are building tools for analyzing protein-protein and cell-to-cell interactions. To identify genes involved in myelination and MS, we developed a bioinformatics-based strategy, interactome analysis, which combines proteome and gene expression methodologies. Identified genes are evaluated in peripheral blood of MS patients and in mouse models. To accelerate drug discovery, 23 machine learning-based methodologies were assessed for myelin identification in fluorescent microscopy images. Results: Interactome analysis identified hundreds of proteinprotein interactions between pairs of oligodendrocytes, neurons, macrophages, microglia, and T cells. Most significant interactions are further analyzed in vivo and in vitro. Our customizedconvolutional neural network and Boosted Tree methods segmented myelin at over 98% accuracy. Identified myelin can be quantified and visualized in 3D. Conclusion: The interactome analysis yielded novel genes that are likely to be linked to MS. Machine learning-based methodologies are very effective in accelerating myelin quantification and thus, drug screens against demyelinating diseases such as MS. Overall, our innovative analysis strategies employing computer assistance produced novel avenues of exploration for myelination and demyelinating diseases. This study was supported TUBITAK (218S495,316S026), Istanbul Medipol University (BAP2018/06), and Turkish Academy of Sciences (GEBIP). [ABSTRACT FROM AUTHOR]
- Published
- 2020
13. A multi-spectral myelin annotation tool for machine learning based myelin quantification.
- Author
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Çapar A, Çimen S, Aladağ Z, Ekinci DA, Ayten UE, Kerman BE, and Töreyin BU
- Subjects
- Neural Networks, Computer, Axons, Software, Myelin Sheath, Machine Learning
- Abstract
Myelin is an essential component of the nervous system and myelin damage causes demyelination diseases. Myelin is a sheet of oligodendrocyte membrane wrapped around the neuronal axon. In the fluorescent images, experts manually identify myelin by co-localization of oligodendrocyte and axonal membranes that fit certain shape and size criteria. Because myelin wriggles along x-y-z axes, machine learning is ideal for its segmentation. However, machine-learning methods, especially convolutional neural networks (CNNs), require a high number of annotated images, which necessitate expert labor. To facilitate myelin annotation, we developed a workflow and software for myelin ground truth extraction from multi-spectral fluorescent images. Additionally, to the best of our knowledge, for the first time, a set of annotated myelin ground truths for machine learning applications were shared with the community., Competing Interests: No competing interests were disclosed., (Copyright: © 2023 Çapar A et al.)
- Published
- 2023
- Full Text
- View/download PDF
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