Reasearch Awards nomination

Email updates

Keep up to date with the latest news and content from BMC Medical Imaging and BioMed Central.

Open Access Research article

Histological image classification using biologically interpretable shape-based features

Sonal Kothari1, John H Phan2, Andrew N Young34 and May D Wang12*

Author Affiliations

1 Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA

2 Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA

3 Pathology and Laboratory Medicine, Emory University, Atlanta, GA, USA

4 Grady Health System, Atlanta, GA, USA

For all author emails, please log on.

BMC Medical Imaging 2013, 13:9  doi:10.1186/1471-2342-13-9

Published: 13 March 2013

Abstract

Background

Automatic cancer diagnostic systems based on histological image classification are important for improving therapeutic decisions. Previous studies propose textural and morphological features for such systems. These features capture patterns in histological images that are useful for both cancer grading and subtyping. However, because many of these features lack a clear biological interpretation, pathologists may be reluctant to adopt these features for clinical diagnosis.

Methods

We examine the utility of biologically interpretable shape-based features for classification of histological renal tumor images. Using Fourier shape descriptors, we extract shape-based features that capture the distribution of stain-enhanced cellular and tissue structures in each image and evaluate these features using a multi-class prediction model. We compare the predictive performance of the shape-based diagnostic model to that of traditional models, i.e., using textural, morphological and topological features.

Results

The shape-based model, with an average accuracy of 77%, outperforms or complements traditional models. We identify the most informative shapes for each renal tumor subtype from the top-selected features. Results suggest that these shapes are not only accurate diagnostic features, but also correlate with known biological characteristics of renal tumors.

Conclusions

Shape-based analysis of histological renal tumor images accurately classifies disease subtypes and reveals biologically insightful discriminatory features. This method for shape-based analysis can be extended to other histological datasets to aid pathologists in diagnostic and therapeutic decisions.