Ünay, Devrim
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Unay, Devrin
Ünay, D.
Unay, Devrim
Ünay, D.
Unay, Devrim
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devrim.unay@ieu.edu.tr
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05.02. Biomedical Engineering
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Former Staff
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Sustainable Development Goals
1NO POVERTY
0
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2ZERO HUNGER
0
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3GOOD HEALTH AND WELL-BEING
2
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4QUALITY EDUCATION
1
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5GENDER EQUALITY
0
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6CLEAN WATER AND SANITATION
0
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7AFFORDABLE AND CLEAN ENERGY
0
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8DECENT WORK AND ECONOMIC GROWTH
0
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9INDUSTRY, INNOVATION AND INFRASTRUCTURE
4
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10REDUCED INEQUALITIES
0
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11SUSTAINABLE CITIES AND COMMUNITIES
0
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12RESPONSIBLE CONSUMPTION AND PRODUCTION
0
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13CLIMATE ACTION
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14LIFE BELOW WATER
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15LIFE ON LAND
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16PEACE, JUSTICE AND STRONG INSTITUTIONS
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17PARTNERSHIPS FOR THE GOALS
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Documents
109
Citations
1800
h-index
18
Documents
87
Citations
1333
Scholarly Output
33
Articles
8
Views / Downloads
80/217
Supervised MSc Theses
1
Supervised PhD Theses
0
WoS Citation Count
167
Scopus Citation Count
244
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0
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0
WoS Citations per Publication
5.06
Scopus Citations per Publication
7.39
Open Access Source
11
Supervised Theses
1
| Journal | Count |
|---|---|
| 2019 Medıcal Technologıes Congress (Tıptekno) | 4 |
| 2016 Ieee 13Th Internatıonal Symposıum on Bıomedıcal Imagıng (Isbı) | 3 |
| 2017 25th Signal Processing and Communications Applications Conference, SIU 2017 | 2 |
| 26th IEEE Signal Processing and Communications Applications Conference, SIU 2018 | 2 |
| 2019 Scientific Meeting on Electrical-Electronics and Biomedical Engineering and Computer Science, EBBT 2019 | 1 |
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33 results
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Now showing 1 - 10 of 33
Conference Object Dermoscopic Lesion Segmentation Via Optimal Color Channel Fusion(IEEE, 2024-10-10) Okur, Erdem; Unay, Devrim; Turkan, MehmetDeath caused by various kinds of cancer is on rise and skin cancer is one of the most common one worldwide. Due to the importance of early detection, dermoscopy is adopted for visualizing skin lesions and computer-aided detection benefits from these dermoscopic images for better diagnosis results. One of the most important phase of automated skin lesion detection or classification is segmentation, but it is a very challenging task because of several artifacts existing on these images. In this paper, a new method to improve skin lesion segmentation from the existing deep network architectures is proposed, based on the fusion of various results produced by existing models on different color channels. Experimental validations demonstrate that the proposed method increases the average accuracy, on lesion segmentation in terms of Sorensen-Dice and Jaccard indices, when compared to conventional techniques.Conference Object Accurate Dictionary Matching for Mr Fingerprinting Using Neural Networks and Feature Extraction(Institute of Electrical and Electronics Engineers Inc., 2020-10-05) Soyak R.; Ersoy E.O.; Navruz E.; Fakultesi M.; Unay D.; Oksuz I.; Ersoy, Eda Ozgu; Unay, Devrim; Navruz, Ebru; Fakultesi, Muhendislik; Soyak, Refik; Oksuz, IlkayMagnetic Resonance Fingerprinting is a recent technique which aims at providing simultaneous measurements of multiple parameters. MRF works by varying acquisition parameters in a pseudorandom manner so as to get unique, uncorrelated signal evolutions from each tissue. MRF is a dictionary based approach, and thus requires a database. This database can be created by simulating the signal evolutions from first principles using different physical models for a wide variety of tissue parameter combinations. Having this dictionary, a pattern recognition algorithm is used to match the acquired signal evolutions from each voxel with each signal evolution in the dictionary. In this paper, we compare the efficiency of deep learning based feature extraction method and neural network architectures in order to achieve state-of-the-art accuracy in dictionary matching for MRF. Our results showcase successful dictionary matching with high accuracy both quantitatively and qualitatively. © 2020 IEEE.Conference Object Dendritic Spine Classification Based on Two-Photon Microscopic Images Using Sparse Representation(Institute of Electrical and Electronics Engineers Inc., 2016-05) Ghani M.U.; Kanik S.D.; Argunşah A.O.; Israely I.; Ünay D.; Çetin M.Dendritic spines, membranous protrusions of neurons, are one of the few prominent characteristics of neurons. Their shapes change with variations in neuron activity. Spine shape analysis plays a significant role in inferring the inherent relationship between neuron activity and spine morphology variations. First step towards integrating rich shape information is to classify spines into four shape classes reported in literature. This analysis is currently performed manually due to the deficiency of fully automated and reliable tools, which is a time intensive task with subjective results. Availability of automated analysis tools can expedite the analysis process. In this paper, we compare ?1-norm-based sparse representation based classification approach to the least squares method, and the ?2-norm method for dendritic spine classification as well as to a morphological feature-based approach. On a dataset of 242 automatically segmented stubby and mushroom spines, ?1 representation with non-negativity constraint resulted in classification accuracy of 88.02%, which is the highest performance among the techniques considered here. © 2016 IEEE.Conference Object MANUAL AND AUTOMATIC SIZE MEASUREMENT OF LATERAL VENTRICLES AND CENTRAL SULCI AND THEIR COMPLIANCE WITH ATROPHY GRADE(IEEE, 2015) Gokay, Gokhan; Kandemir, Melek; Tepe, M. Savas; Yalciner, Betul; Unay, DevrimDiagnosis and treatment of various brain diseases occurring due to aging such as dementia, take an important role in contemporary research for elderly population in the world and in our country with increasing progressively. Cerebral atrophy is a feature observed in dementia patients and described as neuronal loss or cell death affecting part or all of the brain, to determine the presence and severity of atrophy, experts visually evaluate magnetic resonance images of the brain, especially at locations such as lateral ventricles and central sulci. The aim of this study is measuring the sizes (e.g. length, area and volume) of lateral ventricles and central sulci in 3D and to compare these measurements with experts' atrophy ratings. Lateral ventricle width shows high agreement (around 0.84) with atrophy grades.Article Citation - WoS: 13Citation - Scopus: 15Channel Attention Networks for Robust Mr Fingerprint Matching(IEEE-Inst Electrical Electronics Engineers Inc, 2022-04) Soyak, Refik; Navruz, Ebru; Ersoy, Eda Ozgu; Cruz, Gastao; Prieto, Claudia; King, Andrew P.; Unay, Devrim; Oksuz, IlkayObjective: Magnetic Resonance Fingerprinting (MRF) enables simultaneous mapping of multiple tissue parameters such as T1 and T2 relaxation times. The working principle of MRF relies on varying acquisition parameters pseudo-randomly, so that each tissue generates its unique signal evolution during scanning. Even though MRF provides faster scanning, it has disadvantages such as erroneous and slow generation of the corresponding parametric maps, which needs to be improved. Moreover, there is a need for explainable architectures for understanding the guiding signals to generate accurate parametric maps. Methods: In this paper, we addressed both of these shortcomings by proposing a novel neural network architecture (CONV-ICA) consisting of a channel-wise attention module and a fully convolutional network. Another contribution of this study is a new channel selection method: attention-based channel selection. Furthermore, the effect of patch size and temporal frames of MRF signal on channel reduction are analyzed by employing a channel-wise attention. Results: The proposed approach, evaluated over 3 simulated MRF signals, reduces error in the reconstruction of tissue parameters by 8.88% for T1 and 75.44% for T2 with respect to state-of-the-art methods. Conclusion: It is demonstrated that channel attention mechanism helps to focus on informative channels and fully convolutional network extracts spatial information achieve the best reconstruction performance. Significance: As a consequence of improvement in fast and accurate manner, presented work can contribute to make MRF appropriate for clinical use.Conference Object Citation - WoS: 1Citation - Scopus: 5On Comparison of Manifold Learning Techniques for Dendritic Spine Classification(IEEE, 2016-04) Ghani, Muhammad Usman; Argunsach, Ali Ozgur; Israely, Inbal; Unay, Devrim; Tasdizen, Tolga; Cetin, MujdatDendritic spines are one of the key functional components of neurons. Their morphological changes are correlated with neuronal activity. Neuroscientists study spine shape variations to understand their relation with neuronal activity. Currently this analysis performed manually, the availability of reliable automated tools would assist neuroscientists and accelerate this research. Previously, morphological features based spine analysis has been performed and reported in the literature. In this paper, we explore the idea of using and comparing manifold learning techniques for classifying spine shapes. We start with automatically segmented data and construct our feature vector by stacking and concatenating the columns of images. Further, we apply unsupervised manifold learning algorithms and compare their performance in the context of dendritic spine classification. We achieved 85.95% accuracy on a dataset of 242 automatically segmented mushroom and stubby spines. We also observed that ISOMAP implicitly computes prominent features suitable for classification purposes.Article Citation - WoS: 6Citation - Scopus: 8An Evaluation on the Robustness of Five Popular Keypoint Descriptors To Image Modifications Specific To Laser Scanning Microscopy(IEEE-Inst Electrical Electronics Engineers Inc, 2018) Unay, Devrim; Stanciu, Stefan G.Laser scanning microscopy (LSM) techniques are of paramount importance at this time for key domains such as biology, medicine, or materials science. Computer vision methods are instrumental for boosting the potential of LSM, providing reliable results for important tasks, such as image segmentation, registration, classification, or retrieval in a fraction of the time that a human expert would require (at similar or even higher accuracy levels). Image keypoint extraction and description represent essential building blocks of modern computer vision approaches, and the development of such techniques has gained massive interest over the past couple of decades. In this paper, we compare side-by-side five popular keypoint description techniques, scale invariant feature transform (SIFT), speeded-up robust features (SURF), binary robust invariant scalable keypoints (BRISK), fast retina keypoint (FREAK) and BLOCK, with respect to their capacity to represent in a reproducible manner image regions contained in LSM data sets acquired under different acquisition conditions. We evaluate this capacity in terms of descriptor matching performance, using data sets acquired in a principled manner and a thorough Precision-Recall analysis. We identify which of the five evaluated techniques is most robust to specific LSM image modifications associated to the laser beam power, photomultiplier gain, or pixel dwell, and show that certain pre-processing steps have the potential to enhance keypoint matching.Conference Object Citation - WoS: 1Citation - Scopus: 2A Pattern Mining Approach in Feature Extraction for Emotion Recognition From Speech(Springer International Publishing Ag, 2019) Avcı, Umut; Akkurt, Gamze; Unay, DevrimWe address the problem of recognizing emotions from speech using features derived from emotional patterns. Because much work in the field focuses on using low-level acoustic features, we explicitly study whether high-level features are useful for classifying emotions. For this purpose, we convert a continuous speech signal to a discretized signal and extract discriminative patterns that are capable of distinguishing distinct emotions from each other. Extracted patterns are then used to create a feature set to be fed into a classifier. Experimental results show that patterns alone are good predictors of emotions. When used to build a classifier, pattern features achieve accuracy gains up to 25% compared to state-of-the-art acoustic features.Conference Object Citation - Scopus: 3Diagnosis of Bipolar Disease Using Correlation-Based Feature Selection With Different Classification Methods(IEEE, 2019-10) Cigdem, Ozkan; Sulucay, Aysu; Yilmaz, Arif; Oguz, Kaya; Demirel, Hasan; Kitis, Omer; Eker, Cagdas; Unay, DevrimThree-Dimensional Magnetic Resonance Imaging (3D-MRI) and Computer-Aided Detection (CAD) have been widely studied in the detection of bipolar disorder (BD). In this study, the structural alterations at the grey matter (GM) and white matter (WM) of BD subjects versus healthy controls (HCs) have been compared using Voxel-Based Morphometry (VBM). In order to obtain 3D GM and WM masks, the two sample t-test method and total intracranial volumes of BD and HC as a covariate have been utilized. In addition to analyzing effects of GM and WM tissue maps separately in the detection of BD, impacts of both GM and WM ones are studied by concatenating them in a matrix. The correlation-based feature selection (CFS) feature ranking method is applied to the obtained 3D masks to rank the features, the number of selected top-ranked features are determined using a Fisher criterion (FC) approach, and different classification algorithms are used to classify BD apart from HCs. In this study, 26 BDs and 38 HCs data are used. The experimental results indicate that the classification accuracy of Naive Bayes outperforms the other four classification algorithms used in this study. Additionally, concatenation of GM and WM tissue maps enhances the classification performances of using GM-only and WM-only ones. The classification accuracies obtained for GM, WM, and their concatenation are 72.92%, 78.33%, and 80.00% respectively.Conference Object Manual and Automatic Size Measurement of Lateral Ventricle and Central Sulci and Their Compliance With Atrophy Grade(Institute of Electrical and Electronics Engineers Inc., 2015-10) Gökay G.; Kandemir M.; Tepe M.S.; Yalçiner B.; Ünay D.Diagnosis and treatment of various brain diseases occurring due to aging such as dementia, take an important role in contemporary research for elderly population in the world and in our country with increasing progressively. Cerebral atrophy is a feature observed in dementia patients and described as neuronal loss or cell death affecting part or all of the brain, to determine the presence and severity of atrophy, experts visually evaluate magnetic resonance images of the brain, especially at locations such as lateral ventricles and central sulci. The aim of this study is measuring the sizes (e.g. length, area and volume) of lateral ventricles and central sulci in 3D and to compare these measurements with experts' atrophy ratings. Lateral ventricle width shows high agreement (around 0.84) with atrophy grades. © 2015 IEEE.