Classification of histopathological cancer stem cell images in h&e stained liver tissues

Abstract

Microscopic images are an essential part of cancer diagnosis process in modern medicine. However, diagnosing tissues under microscope is a time-consuming task for pathologists. There is also a signi cant variation in pathologists' decisions on tissue labeling. In this study, we developed a computer-aided diagnosis (CAD) system that classi es and grades H&E stained liver tissue images for pathologists in order to speed up the cancer diagnosis process. This system is designed for H&E stained tissues, because it is cheaper than the conventional CD13 stain. The rst step is labeling the tissue images for classi cation purposes. CD13 stained tissue images are used to construct ground truth labels, because in H&E stained tissues cancer stem cells (CSC) cannot be observed by naked eye. Feature extraction is the next step. Since CSCs cannot be observed by naked eye in H&E stained tissues, we need to extract distinguishing texture features. For this purpose, 20 features are chosen from nine di erent color spaces. These features are fed into a modi ed version of Principal Component Analysis (PCA) algorithm, which is proposed in this thesis. This algorithm takes covariance matrices of feature matrices of images instead of pixel values of images as input. Images are compared in the eigenspace and classi es them according to the angle between them. It is experimentally shown that this algorithm can achieve 76.0% image classi cation accuracy in H&E stained liver tissues for a three-class classi cation problem. Scale invariant feature transform (SIFT), local binary patterns (LBP) and directional feature extraction algorithms are also utilized to classify and grade H&E stained liver tissues. It is observed in the experiments that these features do not provide meaningful information to grade H&E stained liver tissue images. Since our aim is to speed up the cancer diagnosis process, computationally e cient versions of proposed modi ed PCA algorithm are also proposed. Multiplication-free cosine-like similarity measures are employed in the modi ed PCA algorithm and it is shown that some versions of the multiplication-free similarity measure based modi ed PCA algorithm produces better classi cation accuracies than the standard modi ed PCA algorithm. One of the proposed multiplication-free similarity measures achieves 76.0% classi cation accuracy in our dataset containing 454 images of three classes.