Serviks hücre görüntülerinin bölütlenmesi ve sınıflandırılması

Cervical cancer can be prevented if it is detected and treated early. Pap smear test is a manual screening procedure used to detect cervical cancer and precancerous changes in an uterine cervix. However, this procedure is costly and it may result in inaccurate diagnoses due to human error like intra- and inter-observer variability. Therefore, a computer-assisted screening system will be very beneficial to prevent cervical cancer if it increases the reliability of diagnoses.In this thesis, we propose a computer-assisted diagnosis system which helps cyto-technicians by sorting cells in a Pap smear slide according to their abnormality degree. There are three main components of such a system. Firstly, cells along with their nuclei are located using a segmentation procedure on an image taken using a microscope. Then, features describing these segmented cells are extracted. Finally, the cells are sorted according to their abnormality degree based on the extracted features.Different from the related studies that require images of a single cervical cell, we propose a non-parametric generic segmentation algorithm that can also handle images of overlapping cells. We use thresholding as the first phase to extract background regions for obtaining remaining cell regions. The second phase consists of segmenting the cell regions by a non-parametric hierarchical segmentation algorithm that uses the spectral and shape information as well as the gradient information. The last phase aims to partition the cell region into true structures of each nucleus and the whole cytoplasm area by classifying the final segments as nucleus or cytoplasm region. We evaluate our segmentation method both quantitatively and qualitatively using two data sets.By proposing an unsupervised screening system, we aim to approach the problem in a different way when compared to the related studies that concentrate on classification. In order to rank the cells in a Pap slide, we first perform hierarchical clustering on 14 different cell features. The initial ordering of the cells is determined as the leaf ordering of the constructed hierarchical tree. Then, this initial ordering is improved by applying an optimal leaf ordering algorithm. The experiments with ground truth data show the effectiveness of the proposed approach under different experimental settings.