Investigation into Feasibility of Color and Texture Features for Automated Detection of Lymph Node Metastases in Histopathological Images

Abstract

The purpose of this thesis is to investigate the feasibility of commonly used features in histopathological image analysis for the purpose of locating various carcinoma metastases within lymph nodes, regardless of the tissue from which they originated. Previous work in cancer detection within lymph nodes has been limited to tissue specific classification such as lymphomas or breast cancer metastases. A general carcinoma classifier, one that can discriminate between healthy lymph tissues and many different carcinomas, would enhance pathologist's diagnosing confidence and speed. To investigate a general carcinoma classifier, 24 Hematoxylin \& Eosin stained lymph node images containing 9 different carcinoma types were used to gather 989,531 training examples to train support vector machines. A hue histogram, a texture feature using the local range of pixels, and a combination of hue and texture were tested using 5-fold cross validation on a 250,000 sample subset of the data and subsequently tested on the remaining data. The hue-texture combined feature performed the best, with a cross validation accuracy of 96.26\% and a classification accuracy of 96.90\%, within 1.5\% of the best performing breast cancer metastases detector. For further investigation into feasibility as well as the type of tissues that generate false positives, a probability heatmap for 5 new Hematoxylin \& Eosin stained lymph node images was generated using the hue-texture classifier. This heatmap was used to highlight suspicious areas within a slide. The heatmap received a pathologist rating of 4.8/5 for success in locating metastases, and a 4.2/5 for helpfulness in pathologist's time saving and confidence boosting. In this thesis, the use of color and texture features together proved feasible for discriminating between healthy lymph tissue and carcinoma tissues.