Türkan, Mehmet
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Turkan, Mehmet
Turkan, M
Türkan, M
Turkan, M
Türkan, M
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mehmet.turkan@ieu.edu.tr
mehmet.turkan@gmail.com
mehmet.turkan@gmail.com
Main Affiliation
05.06. Electrical and Electronics Engineering
Status
Current Staff
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Sustainable Development Goals
11
SUSTAINABLE CITIES AND COMMUNITIES
0
Research Products
4
QUALITY EDUCATION
1
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8
DECENT WORK AND ECONOMIC GROWTH
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12
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0
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9
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4
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15
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6
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1
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0
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7
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0
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10
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14
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2
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13
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5
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16
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3
GOOD HEALTH AND WELL-BEING
5
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Documents
55
Citations
968
h-index
15
Documents
47
Citations
617
Scholarly Output
51
Articles
13
Views / Downloads
44/127
Supervised MSc Theses
9
Supervised PhD Theses
2
WoS Citation Count
463
Scopus Citation Count
721
WoS h-index
7
Scopus h-index
9
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0
Projects
0
WoS Citations per Publication
9.08
Scopus Citations per Publication
14.14
Open Access Source
19
Supervised Theses
11
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| Journal | Count |
|---|---|
| 2022 Medıcal Technologıes Congress (Tıptekno'22) | 4 |
| Sıgnal Processıng | 4 |
| Proceedings - International Conference on Image Processing, ICIP | 3 |
| 2024 Medical Technologies Congress -- OCT 10-12, 2024 -- Bodrum, TURKIYE | 2 |
| 2021 Ieee Internatıonal Conference on Image Processıng (Icıp) | 2 |
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51 results
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Now showing 1 - 10 of 51
Conference Object Dermoscopic Lesion Segmentation Via Optimal Color Channel Fusion(IEEE, 2024) 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 Citation - WoS: 1Citation - Scopus: 1Dictionary Learning With Residual Codes(Institute of Electrical and Electronics Engineers Inc., 2017) Oktar Y.; Türkan, MehmetIn conventional sparse representations based dictionary learning algorithms, initial dictionaries are generally assumed to be proper representatives of the system at hand. However, this may not be the case, especially in some systems restricted to random initialization. Therefore, a supposedly optimal state-update based on such an improper model might lead to undesired effects that will be conveyed to successive learning iterations. In this paper, we propose a dictionary learning method which includes a general error-correction process that codes the residual left over from a less intensive initial learning attempt and then adjusts the sparse codes accordingly. Experimental observations show that such additional step vastly improves rates of convergence in high-dimensional cases, also results in better converged states in the case of random initialization. Improvements also scale up with more lenient sparsity constraints. © 2017 IEEE.Conference Object Vision-Based Denim Quality Assessment(Institute of Electrical and Electronics Engineers Inc., 2023) Balkaya, P.; Basut, M.C.; Türkan, MehmetManual color quality control is a slow process in textile industry. The color separation of denim products is judged by human operators to decide the correct chemical process to be applied such as ozone washing, to obtain the desired colors and textural characteristics for end-users. This paper aims at designing a vision-based automated system for grouping industrial denim jeans in order to eliminate human error or any other external factor, and increase efficiency and reliability of the process. Firstly, uniquely identifying features are extracted from images of the products taken in similar conditions using an image capture system, that is specifically designed for it, then parameters of clustering algorithms such as the number of clusters are decided by considering different validity scores. Finally, using the outcomes of several algorithms, a clustering is made from a batch of denim products and then the results are sent to human operators to be evaluated. © 2023 IEEE.Master Thesis Hücre Sayım Uygulamaları için Akıllı Telefon Tabanlı Otomatize Opto-Akışkan Platform(2025) Avcı, Meryem Beyza; Türkan, MehmetHücre analiz teknolojileri, hücre canlılığı, yoğunluğu ve konfluens gibi temel parametrelerin yüksek doğrulukta değerlendirilmesini sağlayarak biyomedikal araştırmalarda önemli bir rol oynamaktadır. Gelişmiş ticari çözümlerin mevcudiyetine rağmen, birçok sistem yüksek maliyetler, karmaşık donanım gereksinimleri ve çeşitli deneysel ortamlarda kısıtlı uyarlanabilirlik nedeniyle sınırlı kalmaktadır. Bu sınırlamalara çözüm bulmak için; düşük maliyetli optik bileşenleri, otomatik sıvı yönetimini ve uyarlanabilir görüntü işleme algoritmalarını entegre eden, akıllı telefon tabanlı otomatik bir optoakışkan platform geliştirilmiştir. Platformun tasarımı, sade bir donanım yapısıyla güçlü analiz kabiliyetlerini birleştirmektedir. Sistem, hem tek kullanımlık hem de çok kullanımlık modları destekleyen temizlenebilir bir akış haznesi içermekte olup maliyet etkinliği ve esneklik sağlamaktadır. Platforma özel olarak geliştirilen mobil uygulama Qtouch; gerçek zamanlı donanım kontrolü, Bluetooth destekli iletişim ve sunucu tabanlı görüntü işlemeyi entegre ederek işlemleri kolaylaştırmaktadır. Görüntü işleme süreci, çoklu pozlama birleştirme, uyarlanabilir eşikleme ve morfolojik filtreleme teknikleri ile ham görüntü verilerini geliştirmektedir. Bu aşamalar, hücre morfolojisi veya kümelenme durumundan bağımsız olarak hücrelerin doğru şekilde bölütlenmesini sağlamakta ve derin öğrenme modellerine ya da önceden tanımlanmış parametrelere olan gereksinimi ortadan kaldırmaktadır. Farklı hücre tipleriyle gerçekleştirilen doğrulama deneyleri, platformun hücre sayımında yüksek doğruluk sağladığını ortaya koymuş; sonuçların, hücre analizinde altın standart olarak kabul edilen akış sitometrisine göre %5'ten daha az sapma gösterdiği belirlenmiştir. Platform, yüksek verimli uygulamalara uyum sağlayabilecek kapasitede olup her bir testte 10.000'den fazla hücreyi işleyebilmekte ve istatistiksel varyasyonu en aza indirmektedir. Platform, uygun maliyetli, uyarlanabilir ve yüksek doğrulukta bir çözüm sunarak ileri düzey laboratuvar sistemleri ile kaynağı kısıtlı araştırma ortamları arasında etkili bir geçiş sağlamaktadır.Conference Object Citation - Scopus: 1Patch Enhancement for Melanoma Detection With Bag of Visual Words(IEEE, 2022) Okur, Erdem; Turkan, MehmetMelanoma is a type of skin cancer caused by the ultraviolet radiation of Sun. Melanoma will become severe if it is not detected early, and it may spread to other body organs, most commonly the lungs, brain, liver, and bones. Dermatologists look for tell-tale signs of melanoma on pigmented skin lesions (moles) to detect it or, in some cases, differentiate it from other skin diseases. Unfortunately, imprecise subjective analysis may result in a series of biopsies that are unnecessary. Furthermore, this type of imprecision may allow a melanoma case to spread undetected. This study develops an automatic melanoma detection system to overcome this challenge. The proposed method is based on Bag of Visual Words (BoVW) with a new patch enhancement scheme, which incorporates both traditional and cutting-edge methods. Experimental comparisons between the proposed method and the well-known convolutional neural network models demonstrate the effectiveness of the developed system.Conference Object Citation - Scopus: 1Binocular Vision Based Convolutional Networks(Institute of Electrical and Electronics Engineers Inc., 2020) Oktar Y.; Ulucan O.; Karakaya D.; Ersoy E.O.; Türkan, MehmetIt is arguable that whether the single camera captured (monocular) image datasets are sufficient enough to train and test convolutional neural networks (CNNs) for imitating the biological neural network structures of the human brain. As human visual system works in binocular, the collaboration of the eyes with the two brain lobes needs more investigation for improvements in such CNN-based visual imagery analysis applications. It is indeed questionable that if respective visual fields of each eye and the associated brain lobes are responsible for different learning abilities of the same scene. There are such open questions in this field of research which need rigorous investigation in order to further understand the nature of the human visual system, hence improve the currently available deep learning applications. This paper analyses a binocular CNNs architecture that is more analogous to the biological structure of the human visual system than the conventional deep learning techniques. While taking a structure called optic chiasma into account, this architecture consists of basically two parallel CNN structures associated with each visual field and the brain lobe, fully connected later possibly as in the primary visual cortex. Experimental results demonstrate that binocular learning of two different visual fields leads to better classification rates on average, when compared to classical CNN architectures. © 2020 IEEE.Article Citation - WoS: 30Citation - Scopus: 31Multi-Exposure Image Fusion Based on Linear Embeddings and Watershed Masking(Elsevier, 2021) Ulucan, Oguzhan; Karakaya, Diclehan; Turkan, MehmetHigh dynamic range imaging (HDRI) is a challenging technology but yet demanding for modern imaging applications. Low-cost image sensors have limited dynamic range, and it is not always possible to capture and display natural scenes with high contrast and information loss in any exposure is inevitable. Three solutions for HDRI are using expensive high dynamic range (HDR) cameras with HDR-compatible displays, tone mapping operators for low dynamic range (LDR) screens, and capturing and fusing multiple exposures of the same LDR scene via image fusion algorithms. Companies that produce user grade devices prefer multi-exposure fusion (MEF) approaches to obtain HDR-like images for LDR screens due to its low cost. Hence, merging a stack of images containing different exposures of the same scene into a single informative image is an attractive research field. In this study, a novel, simple yet effective method is proposed for static image exposure fusion. The developed technique is based on weight map extraction via linear embeddings and watershed masking. The main advantage lies in watershed masking-based adjustment for obtaining accurate weights for image fusion. The comprehensive experimental comparisons demonstrate very strong visual and statistical results, and this approach should facilitate future MEF studies. (C) 2020 Elsevier B.V. All rights reserved.Master Thesis Optimized Exemplar-Based Light Field Super-Resolution(İzmir Ekonomi Üniversitesi, 2023) Aydeniz, Burhan; Türkan, MehmetIşık 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.Conference Object Citation - WoS: 2Citation - Scopus: 2Nled: Neighbor Linear-Embedding Denoising for Fluorescence Microscopy Images(IEEE, 2022) Kirmiziay, Cagatay; Aydeniz, Burhan; Turkan, MehmetAs 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.Conference Object Citation - WoS: 2Citation - Scopus: 2MICROSCALE IMAGE ENHANCEMENT VIA PCA AND WELL-EXPOSEDNESS MAPS(IEEE Computer Society, 2022) Yayci Z.O.; Dura U.; Kaya Z.B.; Cetin A.E.; Türkan, MehmetThe restrictions of accessing high-end microscopes, microscale cameras and high-tech imaging lenses result in a high demand on low-cost microscopes. However, low-cost microscopes are facing with many image capture and quality limitations due to incompatible equipped instrumentation. This study aims at overcoming illumination and contrast problems, color aberration issues, and blur and noise corruption in low-cost microscopes at high image magnification rates. The three color channels of the input image are enhanced via principal component analysis and well-exposedness feature maps by means of cross-channel histogram matching, Laplacian and non-local means filtering. The proposed approach produces sharper, and better color and illumination fixed outputs when compared to existing methods in literature. © 2022 IEEE.
