The proposed shape features and TIPs improved the HCFs and DFs, respectively, and the feature concatenation further enhanced the quality of features for differentiating AMLwvf from ccRCC in abdominal CE CT images.

We propose a computer-aided classification system for distinguishing fp-AML from ccRCC using machine learning classifiers with quantitative texture features. Our contribution is to investigate the proper combination between the quantitative features and classification systems on the CE MDCT images. In experiments, it can be demonstrated that (a) the features based on histogram characteristics on bright intensity region and texture patterns on inhomogeneity inside masses were selected as key features to classify fp-AML and ccRCC, and (b) the proper combination of feature selection and classification methods achieved high performance in differentiating benign from malignant masses. The proposed classification system can be used to assess the useful features associated with the malignancy for renal masses in CE MDCT images.

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