A. Haridas, and
Proc. IEEE International Conference on Bioinformatics and Biomedicine (BIBM),
It is increasingly appreciated that the tumor stroma
is an integral part of cancer initiation, growth, and progression.
Recently it has been shown that the stromal elements of tumors
hold prognostic as well as response-predictive information. This
work proposes a multi-scale image analysis and machine learning
pipeline for epithelial versus stromal tissue identification in
images of H&E stained breast cancer specimens. Unlike many
studies that perform pixel or block-based epithelium-stroma classification,
this pipeline includes an explicit image segmentation
module. We first partition the H&E stain images into coherent
partitions/superpixels, then extract a number of regional color
and texture features from these partitions, and finally use support
vector machine classifiers to classify them into epithelium and
stroma classes. We propose a multi-scale hierarchical fuzzy cmeans
(HFCM) approach for segmentation of the images. We also
investigate multi-scale feature extraction and descriptors. Our
experimental results on Stanford Tissue Microarray Database
show that multi-scale regional feature descriptors outperform
single-scale feature descriptors. Experimental results also show
that when the same set of regional features are used, classification
of HFCM-based partitions outperforms classification of both
regular blocks and SLIC superpixels.
author = "F. Bunyak and A. Hafiane and Z. Al-Milagi and I. Ersoy and A. Haridas and K. Palaniappan",
title = "A segmentation-based multi-scale framework for the classification of epithelial and stromal tissues in H&E images",
year = 2015,
booktitle = "Proc. IEEE International Conference on Bioinformatics and Biomedicine (BIBM)",
pages = "450-453",
keywords = "biomedical, histopathology, segmentation, classification",
url = "http://cci.drexel.edu/ieeebibm/bibm2015/index.html"
F. Bunyak, A. Hafiane, Z. Al-Milagi, I. Ersoy, A. Haridas, and K. Palaniappan. A segmentation-based multi-scale framework for the classification of epithelial and stromal tissues in H&E images. Proc. IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pages 450-453, 2015.