WoS İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394

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Author: search.filters.author.Tasdemir, KasimWoS Q: search.filters.wosQuartile.Q1

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  • Article
    Citation - WoS: 15
    Citation - Scopus: 22
    Mitigation of H.264 and H.265 Video Compression for Reliable PRNU Estimation
    (IEEE-Inst Electrical Electronics Engineers Inc, 2020) Altinisik, Enes; Tasdemir, Kasim; Sencar, Husrev Taha
    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.
  • Article
    Citation - WoS: 14
    Citation - Scopus: 15
    Image-Analysis Based Readout Method for Biochip: Automated Quantification of Immunomagnetic Beads, Micropads and Patient Leukemia Cell
    (Pergamon-Elsevier Science Ltd, 2020-06) Uslu, Fatma; Icoz, Kutay; Tasdemir, Kasim; Dogan, Refika S.; Yilmaz, Bulent
    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.
  • Article
    Citation - WoS: 15
    Citation - Scopus: 14
    A Review of Mammographic Region of Interest Classification
    (Wiley Periodicals, inc, 2020-02-18) Yengec Tasdemir, Sena B.; Tasdemir, Kasim; Aydin, Zafer
    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. This article is categorized under: Application Areas > Science and Technology Technologies > Machine Learning Technologies > Classification