A new deformable model-based segmentation approach for accurate extraction of the kidney from abdominal CT images

Kidney segmentation is an essential step in developing any non-invasive Computer-Assisted Diagnostic (CAD) system for the early detection of acute renal rejection. This paper describes a 3-D approach for kidney segmentation from abdominal Computed Tomography (CT) images using a level set-based deformable model. Its evolution is controlled by a specially designed stochastic speed function that accounts for a shape prior and features of image intensity and spatial interactions. The shape prior is learned from the co-aligned 3-D kidney data. The current visual appearances are described with marginal gray level distributions obtained by separating their mixture over the kidney data. The spatial interactions between the kidney voxels are modeled by a 3-D 2^nd-order translation and rotation variant Markov-Gibbs Random Field (MGRF) of “object-background” labels with analytically estimated potentials. The proposed approach has been evaluated on the CT data sets of 29 patients, yielding an average volumetric overlap error of 3.71%. The presented results indicate that combing CT images' characteristics into level set evolution leads to more accurate segmentation results.