Histopatolojik imgeler için imge işleme algoritmaları

Conventionally, a pathologist examines cancer cell morphologies under microscope. This process takes a lot of time and is subject to human mistakes. Computer aided diagnosis (CAD) systems and modules aim to help pathologists in their work to decrease the time consumption and the human mistakes. This thesis proposes a CAD module and algorithms which assist the pathologist in segmentation, detection and the classification problems in histopatholgic images. A multi-resolution super-pixel based segmentation algorithm is developed to measure the cell size, count the number of cells and track the motion of cells in Mesenchymal Stem Cell (MSC) images. The proposed algorithm is compared with Simple Linear Iterative Clustering (SLIC) algorithm. It is experimentally observed that in the segmentation stage, the cell detection rate is increased by 7% and the false alarm is decreased by 5%. In addition to this, two novel decision rules for merging similar neighboring super-pixels are proposed. One dimensional version of the Scale Invariant Feature Transform (SIFT) based merging algorithm is developed and applied to the histograms of the neighboring super-pixels to determine the similar regions. It is also shown that the merging process can be made with the use of wavelets. Moreover, it is shown that region covariance and codifference matrices can be used in detection of cancer stem cells (CSC) and a CAD module for the CSC detection in liver cancer tissue images are developed. The system locates CSCs in CD13 stained liver tissue images. The method has an online learning approach which improves the accuracy of detection. It is experimentally shown that, applying the proposed approach with the user guidance,increases the overall detection quality and accuracy up to 25% compared to using region descriptors alone. Also, the proposed module is compared with the similar plug-ins of ImageJ and Fiji. It is shown that, when the similar features are used, the implemented module achieves approximately 20% better classification results compared to the plug-ins of Imagej and Fiji. Furthermore, the proposed 1-D SIFT algorithm is expanded and used in classification of the cancer tissues images stained with Hematoxylin and Eosin (H&E) stain, which is a cost effective routine compared to the immunohistochemistry (IHC) procedure. The 1-D SIFT algorithm is able to classify healthy and cancerous tissue images with up to 91% accuracy in H&E stained images in our data set.