Automated grading of breast cancer histopathology using spectral clustering with textural and architectural image features

In this paper we present a novel image analysis methodology for automatically distinguishing low and high grades of breast cancer from digitized histopathology. A set of over 3,400 image features, including textural and nuclear architecture based features, are extracted from a database of 48 breast biopsy tissue studies (30 cancerous and 18 benign images). Spectral clustering is used to reduce the dimensionality of the feature set. A support vector machine (SVM) classifier is used (1) to distinguish between cancerous and non-cancerous images, and (2) to distinguish between images containing low and high grades of cancer. Classification is repeated using different subsets of features to compare their performance. The system achieves a 95.8% accuracy in distinguishing cancer from non-cancer using texture-based characteristics (Gabor filter features), and 93.3% accuracy in distinguishing high from low grades of cancer using architectural features. In addition, we investigate the underlying main-fold structure on which the different grades of breast cancer lie as revealed through spectral clustering. The manifold shows a smooth spatial transition from low to high grade breast cancer.