An analysis of scale and rotation invariance in the bag-of-features method for histopathological image classification

Authors:
S. Hussain Raza;R. Mitchell Parry;Richard A. Moffitt;Andrew N. Young;May D. Wang
Affiliations:
School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA;The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA;The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA;Pathology and Laboratory Medicine, Emory University, Atlanta, GA;School of Electrical and Computer Engineering, Georgia Institute of Technology and The Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University ...
Venue:
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part III
Year:
2011

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Abstract

The bag-of-features method has emerged as a useful and flexible tool that can capture medically relevant image characteristics. In this paper, we study the effect of scale and rotation invariance in the bag-offeatures framework for Renal Cell Carcinoma subtype classification. We estimated the performance of different features by linear support vector machine over 10 iterations of 3-fold cross validation. For a very heterogeneous dataset labeled by an expert pathologist, we achieve a classification accuracy of 88% with four subtypes. Our study shows that rotation invariance is more important than scale invariance but combining both properties gives better classification performance.