title: 3D kidney segmentation from CT images using a level set approach guided by a novel stochastic speed function.
abstract: Kidney segmentation is a key step in developing any noninvasive computer-aided diagnosis (CAD) system for early detection of acute renal rejection. This paper describes a new 3-D segmentation approach for the kidney from computed tomography (CT) images. The kidney borders are segmented from the surrounding abdominal tissues with a geometric deformable model guided by a special stochastic speed relationship. The latter accounts for a shape prior and appearance features in terms of voxel-wise image intensities and their pair-wise spatial interactions integrated into a two-level joint Markov-Gibbs random field (MGRF) model of the kidney and its background. The segmentation approach was evaluated on 21 CT data sets with available manual expert segmentation. The performance evaluation based on the receiver operating characteristic (ROC) and Dice similarity coefficient (DSC) between manually drawn and automatically segmented contours confirm the robustness and accuracy of the proposed segmentation approach.
authors: BioImaging Laboratory, Bioengineering Department, University of Louisville, Louisville, KY, USA.
authors: Fahmi
authors: F
authors: Khalifa
authors: Ahmed
authors: A
authors: Elnakib
authors: Garth M
authors: GM
authors: Beache
authors: Georgy
authors: G
authors: Gimel'farb
authors: Mohamed Abo
authors: MA
authors: El-Ghar
authors: Rosemary
authors: R
authors: Ouseph
authors: Guela
authors: G
authors: Sokhadze
authors: Samantha
authors: S
authors: Manning
authors: Patrick
authors: P
authors: McClure
authors: Ayman
authors: A
authors: El-Baz
mesh_terms: D000465:Algorithms; D001331:Automation; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D007668:Kidney / Q000000981:diagnostic imaging* / Q000473:pathology*; D008390:Markov Chains; D015233:Models, Statistical; D010363:Pattern Recognition, Automated; D011336:Probability; D012372:ROC Curve; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D013269:Stochastic Processes; D014057:Tomography, X-Ray Computed / Q000379:methods*