Automated Renal Cell Carcinoma Subtype Classification Using Morphological, Textural and Wavelets Based Features

Authors:
Qaiser Chaudry;Syed Hussain Raza;Andrew N. Young;May D. Wang
Affiliations:
Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, USA;Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, USA;Pathology and Laboratory Medicine, Emory University, Atlanta, USA;Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, USA and Biomedical Engineering, Winship Cancer Institute, Georgia Institute of Technology and Emory University, Atlan ...
Venue:
Journal of Signal Processing Systems
Year:
2009

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Abstract

We present a new image quantification and classification method for improved pathological diagnosis of human renal cell carcinoma. This method combines different feature extraction methodologies, and is designed to provide consistent clinical results even in the presence of tissue structural heterogeneities and data acquisition variations. The methodologies used for feature extraction include image morphological analysis, wavelet analysis and texture analysis, which are combined to develop a robust classification system based on a simple Bayesian classifier. We have achieved classification accuracies of about 90% with this heterogeneous dataset. The misclassified images are significantly different from the rest of images in their class and therefore cannot be attributed to weakness in the classification system.