Detection and segmentation of small renal mass (SRM) in renal CT images are important pre-processing for computer-aided diagnosis of renal cancer. However, the task is known to be challenging due to its variety of size, shape, and location. In this paper, we propose an automated method for detecting and segmenting SRM in contrast-enhanced CT images using texture and context feature classification. First, kidney ROIs are determined by intensity and location thresholding. Second, mass candidates are extracted by intensity and location thresholding. Third, false positive reduction is applied with patch-based texture and context feature classification. Finally, mass segmentation is performed, using the detection results as a seed, with region growing, active contours, and outlier removal with size and shape criteria. In experiments, our method detected SRM with specificity and PPV of 99.63% and 64.2%, respectively, and segmented them with sensitivity, specificity, and DSC of 89.91%, 98.96% and 88.94%, respectively.