Mikroskopik görüntülerin bilgisayar destekli yorumlanması için imge işleme yöntemleri

Image processing algorithms for automated analysis of microscopic images havebecome increasingly popular in the last decade with the remarkable growth incomputational power. The advent of high-throughput scanning devices allowsfor computer-assisted evaluation of microscopic images, resulting in a quick andunbiased image interpretation that will facilitate the clinical decision-making process.In this thesis, new methods are proposed to provide solution to two imageanalysis problems in biology and histopathology.The first problem is the classification of human carcinoma cell line images.Cancer cell lines are widely used for research purposes in laboratories all overthe world. In molecular biology studies, researchers deal with a large numberof specimens whose identity have to be checked at various points in time. Anovel computerized method is presented for cancer cell line image classification.Microscopic images containing irregular carcinoma cell patterns are representedby subwindows which correspond to foreground pixels. For each subwindow,a covariance descriptor utilizing the dual-tree complex wavelet transform (DTCWT)coefficients as pixel features is computed. A Support Vector Machine(SVM) classifier with radial basis function (RBF) kernel is employed for finalclassification. For 14 different classes, we achieve an overall accuracy of 98%,which outperforms the classical covariance based methods.Histopathological image analysis problem is related to the grading of follicularlymphoma (FL) disease. FL is one of the commonly encountered cancer types inthe lymph system. FL grading is based on histological examination of hematoxilinand eosin (H&E) stained tissue sections by pathologists who make clinical decisionsby manually counting the malignant centroblast (CB) cells. This gradingmethod is subject to substantial inter- and intra-reader variability and samplingbias. A computer-assisted method is presented for detection of CB cells in H&EstainedFL tissue samples. The proposed algorithm takes advantage of the scalespacerepresentation of FL images to detect blob-like cell regions which reside inthe scale-space extrema of the difference-of-Gaussian images. Multi-stage falsepositive elimination strategy is employed with some statistical region propertiesand textural features such as gray-level co-occurrence matrix (GLCM), gray-levelrun-length matrix (GLRLM) and Scale Invariant Feature Transform (SIFT). Thealgorithm is evaluated on 30 images and 90% CB detection accuracy is obtained,