Abstract

Histology imaging is the cornerstone for confirming and understanding cancer. Current practice for examination and interpretation of histopathology images requires highly trained anatomic pathologists. The identification of cancerous image patches is time-consuming, suffers from large inter- and intra- pathologist variation; thus there is an increasing interest in automatizing this part of cancer screening. We propose an automatic approach to classify the image patches into cancerous and non-cancerous, when multiple image patches are taken per subject. Our methodology comprises two main steps. First we summarize the image patches using a set of spatially indexed vector-valued functions which are modeled to extract the main features that capture the image patch - specific functional and the spatial variation. Second, these features are then used in a classification approach to identify cancerous image patches. We illustrate the performance of the method through simulations and present the results on a data set containing cancerous and non-cancerous H&E stained patches collected from whole slide images of prostate tissue for many patients.