School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA.
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-of-features 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.
Written by:
Raza SH, Parry RM, Moffitt RA, Young AN, Wang MD. Are you the author?
Reference: Med Image Comput Comput Assist Interv. 2011;14(Pt 3):66-74.
PubMed Abstract
PMID: 22003685
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