Browsing by Author "Tasdemir, Kasim"

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Automated quantification of immunomagnetic beads and leukemia cells from optical microscope images
(ELSEVIER SCI LTD, THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND, 2019) Uslu, Fatma; Icoz, Kutay); Tasdemir, Kasim; Yilmaz, Bulent; 0000-0003-4542-2728; 0000-0002-0947-6166; AGÜ, Mühendislik Fakültesi, Elektrik - Elektronik Mühendisliği Bölümü
Quantification of tumor cells is crucial for early detection and monitoring the progress of cancer. Several methods have been developed for detecting tumor cells. However, automated quantification of cells in the presence of immunomagnetic beads has not been studied. In this study, we developed computer vision based algorithms to quantify the leukemia cells captured and separated by micron size immunomagnetic beads. Color, size based object identification and machine learning based methods were implemented to quantify targets in the images recorded by a bright field microscope. Images acquired by a 40x or a 20x objective were analyzed, the immunomagnetic beads were detected with an error rate of 0.0171 and 0.0384 respectively. Our results reveal that the proposed method attains 91.6% precision for the 40x objective and 79.7% for the 20x objective. This algorithm has the potential to be the signal readout mechanism of a biochip for cell detection. (C) 2019 Elsevier Ltd. All rights reserved.
BLSTM based night-time wildfire detection from video
(Public Library of Science, 2022) Agirman, Ahmet K; Tasdemir, Kasim; AGÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü; Ağırman, Ahmet K.; Taşdemir, Kasım
Distinguishing fire from non-fire objects in night videos is problematic if only spatial features are to be used. Those features are highly disrupted under low-lit environments because of several factors, such as the dynamic range limitations of the cameras. This makes the analysis of temporal behavior of night-time fire indispensable for classification. To this end, a BLSTM based night-time wildfire event detection from a video algorithm is proposed. It is shown in the experiments that the proposed algorithm attains 95.15% of accuracy when tested against a wide variety of actual recordings of night-time wildfire incidents and 23.7 ms per frame detection time. Moreover, to pave the way for more targeted solutions to this challenging problem, experiment-based thorough investigations of possible sources of incorrect predictions and discussion of the unique nature of night-time wildfire videos are presented in the paper.
Deep learning based semantic segmentation and quantification for MRD biochip images
(ELSEVIER SCI LTD, 2022) Çelebi, Fatma; Tasdemir, Kasim; Icoz, Kutay; 0000-0002-0947-6166; AGÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü; Çelebi, Fatma; Tasdemir, Kasim; Icoz, Kutay
Microfluidic platforms offer prominent advantages for the early detection of cancer and monitoring the patient response to therapy. Numerous microfluidic platforms have been developed for capturing and quantifying the tumor cells integrating several readout methods. Earlier, we have developed a microfluidic platform (MRD Biochip) to capture and quantify leukemia cells. This is the first study which employs a deep learning-based segmentation to the MRD Biochip images consisting of leukemic cells, immunomagnetic beads and micropads. Implementing deep learning algorithms has two main contributions; firstly, the quantification performance of the readout method is improved for the unbalanced dataset. Secondly, unlike the previous classical computer visionbased method, it does not require any manual tuning of the parameters which resulted in a more generalized model against variations of objects in the image in terms of size, color, and noise. As a result of these benefits, the proposed system is promising for providing real time analysis for microfluidic systems. Moreover, we compare different deep learning based semantic segmentation algorithms on the image dataset which are acquired from the real patient samples using a bright-field microscopy. Without cell staining, hyper-parameter optimized, and modified U-Net semantic segmentation algorithm yields 98.7% global accuracy, 86.1% mean IoU, 92.2% mean precision, 92.2% mean recall and 92.2% mean F-1 score measure on the patient dataset. After segmentation, quantification result yields 89% average precision, 97% average recall on test images. By applying the deep learning algorithms, we are able to improve our previous results that employed conventional computer vision methods.
EXTRACTING PRNU NOISE FROM H.264 CODED VIDEOS
(IEEE COMPUTER SOC, 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1264 USA, 2018) Altinisik, Enes; Tasdemir, Kasim; Sencar, Husrev Taha; 0000-0003-4542-2728; AGÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü
Every device equipped with a digital camera has a unique identity. This phenomenon is essentially due to a systematic noise component of an imaging sensor, known as photo-response non-uniformity (PRNU) noise. An imaging sensor inadvertently introduces this noise pattern to all media captured by that imaging sensor. The procedure for extracting PRNU noise has been well studied in the context of photographic images, however, its extension to video has so far been neglected. In this work, considering H.264 coding standard, we describe a procedure to extract sensor fingerprint from non-stabilized videos. The crux of our method is to remove a filtering procedure applied at the decoder to reduce blockiness and to use macroblocks selectively when estimating PRNU noise pattern. Results show that our method has a potential to improve matching performance significantly.
Image-analysis based readout method for biochip: Automated quantification of immunomagnetic beads, micropads and patient leukemia cell
(PERGAMON-ELSEVIER SCIENCE LTD, THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND, 2020) Uslu, Fatma; Icoz, Kutay; Tasdemir, Kasim; Dogan, Refika S.; Yilmaz, Bulent; 0000-0002-0947-6166; 0000-0003-4542-2728; AGÜ, Mühendislik Fakültesi, Elektrik - Elektronik Mühendisliği Bölümü
For diagnosing and monitoring the progress of cancer, detection and quantification of tumor cells is utmost important. Beside standard bench top instruments, several biochip-based methods have been developed for this purpose. Our biochip design incorporates micron size immunomagnetic beads together with micropad arrays, thus requires automated detection and quantification of not only cells but also the micropads and the immunomagnetic beads. The main purpose of the biochip is to capture target cells having different antigens simultaneously. In this proposed study, a digital image processing-based method to quantify the leukemia cells, immunomagnetic beads and micropads was developed as a readout method for the biochip. Color, size-based object detection and object segmentation methods were implemented to detect structures in the images acquired from the biochip by a bright field optical microscope. It has been shown that manual counting and flow cytometry results are in good agreement with the developed automated counting. Average precision is 85 % and average error rate is 13 % for all images of patient samples, average precision is 99 % and average error rate is 1% for cell culture images. With the optimized micropad size, proposed method can reach up to 95 % precision rate for patient samples with an execution time of 90 s per image.
Image-Processing Based Signal Readout Method for MRD Biochip
(IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA, 2019) Uslu, Fatma; Icoz, Kutay; Tasdemir, Kasim; 0000-0002-0947-6166; 0000-0003-4542-2728; AGÜ, Mühendislik Fakültesi, Elektrik - Elektronik Mühendisliği Bölümü
The response of the cancer patients to chemotherapy treatment varies from person to person. For some patients cancer cells are resistant to treatment and these cells can relapse again which is known as minimal residual disease. A microfluidic-based biochip capable of monitoring minimal residual disease is under development by our research group. The role of the biochip is to capture the target cells, which were separated by immunomagnetic beads on micro square tiles. Then biochips are imaged using a bright field optical microscope and it is planned to perform image-processing methods to detect the target cells, immunomagnetic beads and micro tiles. In this work the current progress of image processing methods for differentiating the immunomagnetic beads and micro tiles is presented.
Improved senescent cell segmentation on bright-field microscopy images exploiting representation level contrastive learning
(WILEY Online Library, 2024) Çelebi, Fatma; Boyvat, Dudu; Ayaz-Guner, Serife; Tasdemir, Kasim; Icoz, Kutay; 0000-0003-3157-6806; 0000-0002-1052-0961; 0000-0002-0947-6166; 0000-0001-7472-8297; AGÜ, Mühendislik Fakültesi, Elektrik - Elektronik Mühendisliği Bölümü; Çelebi, Fatma; Boyvat, Dudu; Ayaz-Guner, Serife; Icoz, Kutay
Mesenchymal stem cells (MSCs) are stromal cells which have multi-lineage differentiation and self-renewal potentials. Accurate estimation of total number of senescent cells in MSCs is crucial for clinical applications. Traditional manual cell counting using an optical bright-field microscope is timeconsuming and needs an expert operator. In this study, the senescence cells were segmented and counted automatically by deep learning algorithms. However, well-performing deep learning algorithms require large numbers of labeled datasets. The manual labeling is time consuming and needs an expert. This makes deep learning-based automated counting process impractically expensive. To address this challenge, self-supervised learning based approach was implemented. The approach incorporates representation level contrastive learning component into the instance segmentation algorithm for efficient senescent cell segmentation with limited labeled data. Test results showed that the proposed model improves mean average precision and mean average recall of downstream segmentation task by 8.3% and 3.4% compared to original segmentation model.
Mitigation of H.264 and H.265 Video Compression for Reliable PRNU Estimation
(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA, 2020) Altinisik, Enes; Tasdemir, Kasim; Sencar, Husrev Taha; 0000-0003-4542-2728; AGÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü
The photo-response non-uniformity (PRNU) is a distinctive image sensor characteristic, and an imaging device inadvertently introduces its sensor's PRNU into all media it captures. Therefore, the PRNU can be regarded as a camera fingerprint and used for source attribution. The imaging pipeline in a camera, however, involves various processing steps that are detrimental to PRNU estimation. In the context of photographic images, these challenges are successfully addressed and the method for estimating a sensor's PRNU pattern is well established. However, various additional challenges related to generation of videos remain largely untackled. With this perspective, this work introduces methods to mitigate disruptive effects of widely deployed H.264 and H.265 video compression standards on PRNU estimation. Our approach involves an intervention in the decoding process to eliminate a filtering procedure applied at the decoder to reduce blockiness. It also utilizes decoding parameters to develop a weighting scheme and adjust the contribution of video frames at the macroblock level to PRNU estimation process. Results obtained on videos captured by 28 cameras show that our approach increases the PRNU matching metric up to more than five times over the conventional estimation method tailored for photos. Tests on a public dataset also verify that the proposed method improves the attribution performance by increasing the accuracy and allowing the use of smaller length videos to perform attribution.
Mixture of Learners for Cancer Stem Cell Detection Using CD13 and H&E Stained Images
(SPIE-INT SOC OPTICAL ENGINEERING, 1000 20TH ST, PO BOX 10, BELLINGHAM, WA 98227-0010 USA, 2016) Oguz, Oguzhan; Akbas, Cem Emre; Mallah, Maen; Tasdemir, Kasim; Guzelcan, Ece Akhan; Muenzenmayer, Christian; Wittenberg, Thomas; Uner, Aysegul; Cetin, A. Enis; Atalay, Rengul Cetin; AGÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü;
In this article, algorithms for cancer stem cell (CSC) detection in liver cancer tissue images are developed. Conventionally, a pathologist examines of cancer cell morphologies under microscope. Computer aided diagnosis systems (CAD) aims to help pathologists in this tedious and repetitive work. The first algorithm locates CSCs in CD13 stained liver tissue images. The method has also an online learning algorithm to improve the accuracy of detection. The second family of algorithms classify the cancer tissues stained with H&E which is clinically routine and cost effective than immunohistochemistry (IHC) procedure. The algorithms utilize 1D-SIFT and eigen-analysis based feature sets as descriptors. Normal and cancerous tissues can be classified with 92.1% accuracy in H&E stained images. Classification accuracy of low and high-grade cancerous tissue images is 70.4%. Therefore, this study paves the way for diagnosing the cancerous tissue and grading the level of it using HSLE stained microscopic tissue images.
PRNU Estimation from Encoded Videos Using Block-Based Weighting
(Society for Imaging Science and Technology, 2021) Altinisik, Enes; Tasdemir, Kasim; Sencar, Hüsrev Taha; AGÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü; Taşdemir, Kasım
Estimating the photo-response non-uniformity (PRNU) of an imaging sensor from videos is a challenging task due to complications created by several processing steps in the camera imaging pipeline. Among these steps, video coding is one of the most disruptive to PRNU estimation because of its lossy nature. Since videos are always stored in a compressed format, the ability to cope with the disruptive effects of encoding is central to reliable attribution. In this work, by focusing on the block-based operation of widely used video coding standards, we present an improved approach to PRNU estimation that exploits this behavior. To this purpose, several PRNU weighting schemes that utilize block-level parameters, such as encoding block type, quantization strength, and rate-distortion value, are proposed and compared. Our results show that the use of the coding rate of a block serves as a better estimator for the strength of PRNU with almost three times improvement in the matching statistic at low to medium coding bitrates as compared to the basic estimation method developed for photos
A review of mammographic region of interest classification
(WILEY PERIODICALS, INC, ONE MONTGOMERY ST, SUITE 1200, SAN FRANCISCO, CA 94104 USA, 2020) Yengec Tasdemir, Sena B.; Tasdemir, Kasim; Aydin, Zafer; 0000-0003-4542-2728; AGÜ, Mühendislik Fakültesi, Elektrik - Elektronik Mühendisliği Bölümü
Early detection of breast cancer is important and highly valuable in clinical practice. X-ray mammography is broadly used for prescreening the breast and is also attractive due to its noninvasive nature. However, experts can misdiagnose a significant proportion of the cases, which may either cause redundant examinations or cancer. In order to reduce false positive and negative rates of mammography screening, computer-aided breast cancer detection has been studied for more than 30 years and many methods have been proposed by the researchers. In this review, region of interest (ROI) classification methods, which operate on a predefined or segmented ROIs with a focus on mass classification are surveyed. A total of 72 high quality journal and conference papers are selected from the Web of Science (WOS) database that meet several inclusion criteria. A comparative analysis is provided based on ROI extraction methods, data sets and machine learning techniques employed, the prediction accuracies, and usage frequency statistics. Based on the performances obtained on publicly available data sets, the ROI classification problem from mammogram images can be considered as approaching to be solved. Nonetheless, it can still be used as complementary information in breast cancer detection from the whole mammograms, which has room for improvement.
ROI Detection in Mammogram Images using Wavelet-Based Haralick and HOG Features
(IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA, 2018) Tasdemir, Sena Busra Yengec; Tasdemir, Kasim; Aydin, Zafer; 0000-0003-4542-2728; AGÜ, Mühendislik Fakültesi, Mühendislik Bilimleri Bölümü
Digital mammography is a widespread medical imaging technique that is used for early detection and diagnosis of breast cancer. Detecting the region of interest (ROI) helps to locate the abnormal areas, which may be analyzed further by a radiologist or a CAD system. In this paper, a new classification method is proposed for ROI detection in mammography images. Features are extracted using Wavelet transform, Haralick and HOG descriptors. To reduce the number of dimensions and eliminate irrelevant features, a wrapper-based feature selection method is implemented. Several feature extraction methods and machine learning classifiers are compared by performing a leave-one-image-out cross-validation experiment on a difficult dataset. The proposed feature extraction method provides the best accuracy of 87.5% and the second-best area under curve (AUC) score of 84% when employed in a random forest classifier.
Spatio-Temporal Rich Model-Based Video Steganalysis on Cross Sections of Motion Vector Planes
(Spatio-Temporal Rich Model-Based Video Steganalysis on Cross Sections of Motion Vector Planes, 2016) Tasdemir, Kasim; Kurugollu, Fatih; Sezer, Sakir; AGÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü;
A rich model-based motion vector (MV) steganalysis benefiting from both temporal and spatial correlations of MVs is proposed in this paper. The proposed steganalysis method has a substantially superior detection accuracy than the previous methods, even the targeted ones. The improvement in detection accuracy lies in several novel approaches introduced in this paper. First, it is shown that there is a strong correlation, not only spatially but also temporally, among neighbouring MVs for longer distances. Therefore, temporal MV dependency alongside the spatial dependency is utilized for rigorous MV steganalysis. Second, unlike the filters previously used, which were heuristically designed against a specific MV steganography, a diverse set of many filters, which can capture aberrations introduced by various MV steganography methods is used. The variety and also the number of the filter kernels are substantially more than that of used in the previous ones. Besides that, filters up to fifth order are employed whereas the previous methods use at most second order filters. As a result of these, the proposed system captures various decorrelations in a wide spatio-temporal range and provides a better cover model. The proposed method is tested against the most prominent MV steganalysis and steganography methods. To the best knowledge of the authors, the experiments section has the most comprehensive tests in MV steganalysis field, including five stego and seven steganalysis methods. Test results show that the proposed method yields around 20% detection accuracy increase in low payloads and 5% in higher payloads.