15-02-2013, 03:03 PM
A new deformable model-based segmentation approach for accurate extraction of the kidney from abdominal CT images
ABSTRACT
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 kidneysegmentation 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 kidneydata. 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 2nd-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 CTimages' characteristics into level set evolution leads to more accurate segmentation results.