Browsing by Author "Aydeniz, Burhan"

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Citation - Scopus: 7
Classification of Healthy Siblings of Bipolar Disorder Patients From Healthy Controls Using Mri
(IEEE, 2019) Cigdem, Ozkan; Soyak, Refik; Aydeniz, Burhan; Oguz, Kaya; Demirel, Hasan; Kitis, Omer; Eker, Cagdas
Three Dimensional magnetic resonance imaging (3D-MRI) has been utilized to classify patients with neuroanatomical abnormalities apart from healthy controls (HCs). The studies on the diagnosis of Bipolar Disorder (BD) focuses also on the unaffected relatives of BD patients in order to examine the heritable resistance factors associated with the disorder. Hence, the comparison of Healthy Siblings of Bipolar Disorder patients (HSBDs) and HCs is also required owing to the high heritability of BD. In this paper, the classification of 27HSBDs from 38HCs has been studied by using 3D-MRI and Computer-Aided Detection (CAD). The pre-processing of 3D-MRI data is performed by taking advantage of Voxel-Based Morphometry (VBM) and the structural deformations in the Gray Matter (GM) and White Matter (WM) are obtained by using a general linear model. The model is configured by using a two sample t-test technique and Total Intracranial Volume (TIV) as a covariate. The altered voxels between data groups are considered as Voxel of Interests (VOIs) and the 3D masks are generated for GM and WM tissue probability maps. The Relief-F algorithm is utilized to rank the features and a Fisher Criterion (FC) method is considered to determine the number of top-ranked discriminative features. The performances of Support Vector Machines (SVM) and the Naive Bayes (NB) algorithms are compared on the classification of HSBD and HC. The experiments are performed for GM-only, WM-only, and their combinations. The experimental results indicate that the changes between the brain regions of HSBD and HC might provide information on the heritable factors associated with the BD. Additionally, it is concluded that using the combination of GM and WM tissue probability map provides better results than considering them, separately. Finally, it is obtained that the classification accuracy of SVM on HSBD and HC comparison is better than that of NB.
Citation - WoS: 1
Citation - Scopus: 3
Effects of Covariates on Classification of Bipolar Disorder Using Structural Mri
(IEEE, 2019) Cigdem, Ozkan; Horuz, Erencan; Soyak, Refik; Aydeniz, Burhan; Sulucay, Aysu; Oguz, Kaya; Demirel, Hasan
Three-Dimensional Magnetic Resonance Imaging (3D-MRI) and Computer-Aided Detection (CAD) have been widely studied in the detection and diagnosis of neuroanatomical abnormalities, including bipolar disorder (BD). Pre-processing of 3D-MRI scans plays an important role in post-processing. In this study, Voxel-Based Morphometry (VBM) is used to compare the morphological differences at the grey matter (GM) and white matter (WM) of BD subjects versus healthy controls (HCs). The effects of using different covariates (i.e. total intracranial volume (TIV), age, sex, and their combinations) on classification of BDs from HCs have been investigated for GM-only, WM-only, and their combination. 3D masks for GM and WM are generated separately by using local differences between BPs and HCs and the two sample t-test method. Principle component analysis based dimensionality reduction and support vector machine with Gaussian kernel are employed for classification of 26 BDs and 38 HCs obtained from Ege University, School of Medicine, Department of Psychiatry. The results indicate that using only TIV as a covariate provides more robust results for BD classification compared to other covariate combinations. Furthermore, the combination of GM and WM improves classification performance. The highest classification accuracies obtained for GM, WM, and their combination are 70.30%, 79.70%, and 82.80% respectively.
Enhancing Two-Photon Images for Anatomical Visualisation Using Super-Resolution
(IEEE, 2022) Aydeniz, Burhan; Metin, Safa Can; Turkan, Mehmet; Unay, Devrim; Karayannis, Theofanis; Argunsah, Ali Ozgur
Two-photon Laser Scanning Microscopy (2P-LSM) is a technique used to image the living tissue with relatively high spatio-temporal resolution. However, the time-series images are often corrupted with Poisson-Gaussian noise and deteriorated with motion artifacts. This paper deals with the problem of enhancing 2P-LSM images to reconstruct high quality and high spatial-resolution outputs using the observed time-series stack of low-resolution images. The proposed technique consists of several components including noise filtering, image registration, cell detection and focus measurement, and clustering for a joint denoising and super-resolution. Extensive experiments demonstrate that the proposed method results in gratifying output images containing apparent and clear cell forms at different focus levels.
Fluorescence Microscopy Denoizing Via Neighbor Linear Embedding
(Istanbul University, 2024) Kırmızıay, Çağatay; Aydeniz, Burhan; Türkan, Mehmet
One of the difficulties in studying fluorescence imaging of biological structures is the presence of noise corruption. Even though hardware- and software-related technologies have undergone continual improvement, the unavoidable effect of Poisson–Gaussian mixture type is generally encountered in fluorescence microscopy images. This noise should be mitigated to allow the extraction of valuable information from fluorescence images for various types of biological analysis. Thus, this study introduces a new and efficient learning-based denoizing approach for fluorescence microscopy. The proposed approach is based mainly on linear transformations between noise-free and noisy submanifold structures of patch spaces, benefiting from linear neighbor embeddings of local image patches. According to visual and statistical results, the developed algorithm called "neighbor linear-embedding denoizing" algorithm has a highly competitive and generally superior performance in comparison with the other algorithms used for fluorescence microscopy image denoizing in the literature. © 2024 Istanbul University. All rights reserved.
Citation - WoS: 2
Citation - Scopus: 2
Nled: Neighbor Linear-Embedding Denoising for Fluorescence Microscopy Images
(IEEE, 2022) Kirmiziay, Cagatay; Aydeniz, Burhan; Turkan, Mehmet
As noise corruption is an inevitable issue for all imaging technologies, this problem causes serious difficulties in analyzing the biological fine-details of fluorescence microscopy images. While Gaussian only, Poisson only and mixture of Poisson-Gaussian can generally be observed, the mixed-noise is more prominent in fluorescence microscopy. In this paper, a novel patch-based denoiser-learning approach is proposed for the images captured by fluorescence microscopy. The developed algorithm mainly builds upon linear-embeddings of neighboring image patches, and it learns a linear transformation between noisy and clean intrinsic geometric properties of patch-spaces. Experimental results demonstrate that the proposed Neighbor Linear-Embedding Denoising (NLED) has competitive performance both visually and statistically when compared to other algorithms in literature, for noise corrupted fluorescence microscopy images.
Optimized Exemplar-Based Light Field Super-Resolution
(İzmir Ekonomi Üniversitesi, 2023) Aydeniz, Burhan; Türkan, Mehmet
Işık alanı görüntüleme tekniği, farklı konumlarda ve yönlerde yakalanan ışık hüzmelerinin görüntülerini üretebilir. Çeşitli donanım kısıtlamaları nedeniyle, ışık alanı görüntüleri düşük uzamsal çözünürlüğe sahiptir. Görüntü üretim modelinde, görüntü detaylarının korunması amacıyla, farklı yüksek çözünürlüklü görüntüler tahmin edilebilir. Bu kötü konumlanmış optimizasyon problemini çözmek için literatürde bir cok süper-çözünürlük yöntemi önerilmiştir. Bu tezde, düşük çözünürlüklü görüntülerden çıkarılan örnek yama çiftleri aracılığıyla doğrusal yerleştirme ve dikgen eşleştirme takibi tabanlı algoritmalar kullanılarak örnek tabanlı ışık alanı süper-çözünürlük algoritmaları geliştirilmiştir. Önerilen yöntemler, düşük eşitsizlikli ışık alanı verisetlerinde yüksek çözünürlüklü görüntüleri tahmin etmektedir. İstatistiksel ve görsel sonuçlara göre, en ileri teknoloji algoritmalar ile karşılaştırıldığında önerilen örnek tabanlı ışık alanı süper çözünürlük yaklaşımı dikkate değer bir performans sağlamaktadır.