Browsing by Subject "Microscopy Imaging"

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Open Access
Automated cell analysis in microscopy images
(2018-09) Koyuncu, Can Fahrettin
High-throughput microscopy systems have become popular recently, which facilitate to acquire boundless microscopy images without requiring human intervention. However, the analysis of such amount of images using conventional methods is nearly impractical since the analysis can take up to months. Additionally, a considerable amount of observer variability may occur since the analysis completely relies on interpretation of the analysts. As a remedy for that, automated decision support systems, which are objective and rapid, have gained more attention. Since these systems conduct analyses at cellular level, they require a cell segmentation model, results of which directly affect the performance of the entire system. There are several challenges in cell segmentation, each of which should be addressed carefully in order to have an accurate cell segmentation model. One challenge is that cells can be grown in multilayer on the plate, which makes them appear as clusters on the image. Segmentation of these cells requires extra effort since they should be splitted from each other. Another challenge is the imperfections on the image such as inhomogeneities of pixel intensities in a cell and insu cient pixel intensity differences at the border of overlapping cells. Yetanother challenge is the heterogeneity in the morphological characteristics of cells. Depending on cell line types, cells may appear in various outlooks. Developing a generic cell segmentation model, which can handle different cells' outlooks and imperfections, is an open and challenging problem. In order to tackle with these challenges, we deal with the cell segmentation problem in two parts: (1)We focus on finding a new representation for microscopy images, helping us simplify the cell segmentation problem, so that imperfections in cells and inhomogeneities in their visual properties can be alleviated, and cell locations can be emphasized better. (2) We focus on developing a more advanced cell segmentation method, with the motivation that it is almost impossible to obtain a perfect representation in practice. Thus, we work on developing more sophisticated cell segmentation techniques that overcome deficiencies on the representation. To this end, this thesis introduces three new cell segmentation models, two of which introduce a new cell representation technique as well. In our experiments, we tested our algorithms on various microscopy images obtained under the uorescence and phase contrast microscopies and compared them with the previous cell segmentation methods. Our experiments show that the proposed algorithms are more effective in segmenting cells and more robust to the aforementioned challenges.
Open Access
Image deconvolution methods based on fourier transform phase and bounded energy
(2018-08) Yorulmaz, Onur
We developed deconvolution algorithms based on Fourier transform phase and bounded energy. Deconvolution is a major area of study in image processing applications. In general, restoration of original images from noisy filtered observation images is an ill-posed problem. We use Fourier transform phase as a constraint in developed image recovery methods. The Fourier phase information is robust to noise, which makes it suitable as a frequency domain constraint. One of our focus is microscopy images where the blur is caused by slight disturbances of the focus. Because of the symmetrical optical parameters, it may be assumed that the Point Spread Function (PSF) is symmetrical. This symmetry of PSF results in zero phase distortion in the Fourier transform coefficients of the original image. Since the convolution leads to multiplication in Fourier domain, we assume that the Fourier phase of some of the frequencies of observed image around the origin represents the Fourier phase of the original image in the same set of frequencies. Therefore the Fourier transform phases of the original image can be estimated from the phase of the observed image and this information can be used as a Fourier domain constraint. In order to complete the algorithm, we also use a Total Variation (TV) reduction based regularization in spatial domain. We embed the proposed Fourier phase relation and spatial domain regularization as additional constraints in well-known blind Ayers-Dainty deconvolution method. Another problem we focused on is the restoration of highly blurry Magnetic Particle Imaging (MPI) applications. In this study we developed a standalone iterative algorithm. The algorithm again relies on the symmetry property of the MPI PSF. The phase estimates of the true image are obtained from the observed image. In this case we employ an `1 projection based regularization algorithm. The `1 projection reduces the small coefficients to zero which is suitable for MPI application because the contrast between foreground and background is sufficiently large by nature. Finally, a more general restoration algorithm is developed for deconvolution of non-symmetrical filters. The algorithm uses the known Fourier phase properties of the PSF in order to estimate the Fourier transform phase of the original image. We also update the estimated Fourier transform magnitudes iteratively using the knowledge of observed image and the PSF. A TV reduction based regularization method completes the algorithm in spatial domain. Simulations and experimental results show that the proposed algorithm outperforms the Wiener filter. We also conclude that the addition of estimate of Fourier transform phase is useful in any deconvolution method.