Robust detection of renal calculi from non-contract CT images using TV-flow and MSER features

Abstract

Renal calculi are one of the most painful urologic disorders causing 3 million treatments per year in the United States. The objective of this paper is the automated detection of renal calculi from CT colonography (CTC) images on which they are one of the major extracolonic findings. However, the primary purpose of the CTC protocols is not for the detection of renal calculi, but for screening of colon cancer. The kidneys are imaged with significant amounts of noise in the non-contrast CTC images, which makes the detection of renal calculi extremely challenging. We propose a computer-aided diagnosis method to detect renal calculi in CTC images. It is built on three novel techniques: 1) total variation (TV) flow to reduce image noise while keeping calculi, 2) maximally stable extremal region (MSER) features to find calculus candidates, 3) salient feature descriptors based on intensity properties to train a support vector machine classifier and filter false positives. We selected 23 CTC cases with 36 renal calculi to analyze the detection algorithm. The calculus size ranged from 1.0mm to 6.8mm. Fifteen cases were selected as the training dataset, and the remaining eight cases were used for the testing dataset. The area under the receiver operating characteristic curve (AUC) values were 0.92 in the training datasets and 0.93 in the testing datasets. The testing dataset confidence interval for AUC reported by ROCKIT was [0.8799, 0.9591] and the training dataset was [0.8974, 0.9642]. These encouraging results demonstrated that our detection algorithm can robustly and accurately identify renal calculi from CTC images.