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Optik Saçılma Temelli Rastgele Orman Destekli Parçacık Tespiti ve Sınıflandırılması
(Abdullah Gül Üniversitesi / Fen Bilimleri Enstitüsü, 2023) Genç, Sinan; Genç, Sinan; İçöz, Kutay; Erdem, Talha
Microplastics, tiny plastic particles with sizes smaller than 5 mm., are often found in oceans, rivers, lakes, and atmosphere due to plastic pollution. Microplastics releasing toxic chemicals threaten the environment and harm the aquatic life and humans. Especially, the accumulation of microplastics can have detrimental effects on the food chain as a result of larger organisms consuming smaller organisms. Detecting the microplastics is crucial but also challenging. Over the years, researchers have developed different detection methods. One of the standard methods is using spectroscopy tools such as Fourier transform infrared spectroscopy (FTIR) and Raman spectroscopy. These techniques can identify the chemical composition of microplastics, which can help determine their sources and potential impacts. Another method is the use of microscopy, which allows for the visualization and counting of microplastics in samples. However, these techniques require costly infrastructure, and these instruments being large in size significantly limits the mobility. As a remedy to the cost and mobility problems, in this thesis, we propose and demonstrate a low-cost, portable system to detect size, concentration, and refractive index of microplastics. Our system comprises of low-cost and low-weight components which are utilized for recording the scattering patterns of microplastics in aqueous media. We demonstrate successful predictions of the size and refractive index of microparticles at a given wavelength using a Random Forest Algorithm which relates the measured scattering pattern with the Mie theory. We further employ the refractive index information at various wavelengths for determining the material type of microplastics. We believe that our proposed system enabling an easy, fast, low-cost, and on-site detection of microplastics will be a beneficial tool for the fight against microplastics in the environment.
Biyoçipler için Mikro Biyomalzemelerin ve Hücrelerin Görüntü İşleme Yöntemleri ile Otomatik Olarak Sayılması ve Analizi
(Abdullah Gül Üniversitesi, Fen Bilimleri Enstitüsü, 2023) Çelebi, Fatma; İçöz, Kutay
Quantification of tumor cells is essential for early cancer detection and progression tracking. Multiple techniques have been devised to detect tumor cells. In addition to conventional laboratory instruments, several biochip-based techniques have been devised for this purpose. Our biochip design incorporates micron-sized immunomagnetic beads and micropad arrays, necessitating automated detection and quantification not only of cells but also of the micropads and immunomagnetic beads. The primary function of the biochip is to simultaneously acquire target cells with distinct antigens. As a readout technique for the biochip, this study devised a digital image processing-based method for quantifying leukemia cells, immunomagnetic beads, and micropads. Images were acquired on the chip using bright-field microscopy with image objectives of 20X and 40X. Conventional image processing methods, machine learning methods, and deep learning methods were used to analyze the images. To quantify targets in the images captured by a bright-field microscope, color- and size-based object recognition and machine learning-based methods were first implemented. Secondly, color- and size-based object detection and object segmentation methods were implemented to detect structures in bright-field optical microscope images acquired from the biochip. Third, segmentation of the minimal residual disease (MRD) using deep learning. Implemented biochip images comprised of leukemic cells, immunomagnetic beads, and micropads. Moreover, mesenchymal stem cells (MSCs) are stem cells with the capacity for multilineage differentiation and self-renewal. Estimating the proportion of senescent cells is therefore essential for clinical applications of MSCs. In this study, a self-supervised learning (SSL)-based method for segmenting and quantifying the density of cellular senescence was implemented, which can perform well despite the small size of the labeled dataset.

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