Computerized Renal Cell Carcinoma Nuclear Grading Using 3D Textural Features

An extraction of important features in cancer cell image analysis is a key process in grading renal cell carcinoma. In this study, we applied three-dimensional (3D) texture feature extraction methods to cancer cell nuclei images and evaluated the validity of them for computerized cell nuclei grading. Individual images of 1,800 cell nuclei were extracted from 8 classes of renal cell carcinomas (RCCs) tissues using confocal laser scanning microscopy (CLSM). First, we extracted the chromatin texture quantitatively by calculating 3D gray-level co-occurrence matrices (3D GLCM) and 3D run length matrices (3D GLRLM). To demonstrate the suitability of 3D texture features for grading, we had performed a principal component analysis to reduce feature dimensionality, then, we also performed discriminant analysis as statistical classifier. Finally this result was compared with the result of classification using several optimized features that extracted from stepwise features selection. Additionally AUC (area under curve) analysis was performed for the grade 2 and 3 cell images. Three dimensional texture features have potential for use as fundamental elements in developing a new nuclear grading system with accurate diagnosis and predicting prognosis.