Automated Quantitative Image Analysis of Hematoxylin-Eosin Staining Slides in Lymphoma Based on Hierarchical Kmeans Clustering

Abstract

The microscopic image of tissue section stained by hematoxylin-eosin (HE) is an essential part in histopathology researches. Automated HE image processing remains challenging because forms and distributions of cells and other tissue structures are always extremely irregular with no clear boundaries, especially in conducting high throughput analysis which demands higher accuracy and efficient quantification for the reference of pathologists. To solve this problem, we proposed an automated quantitative image analysis pipeline based on hierarchical clustering of local correlations, which segmented the image into nuclei, cytoplasm and extracellular spaces by classifying image pixels on the basis of local correlation features. Segmentation for precise nucleus boundaries was then performed, and finally a set of indicators characterizing tissue structures were extracted to complete quantification of HE images. Experimental results showed high accuracy and adaptability in cell segmentation despite data variance. Quantitative indicators obtained in this essay provide a reliable evidence for the analysis of HE staining lymphoma pathological image.