3d kidney segmentation from CT images using a level set approach guided by a novel stochastic speed function

Authors:
Fahmi Khalifa;Ahmed Elnakib;Garth M. Beache;Georgy Gimel'farb;Mohamed Abo El-Ghar;Rosemary Ouseph;Guela Sokhadze;Samantha Manning;Patrick McClure;Ayman El-Baz
Affiliations:
BioImaging Laboratory, Bioengineering Department, University of Louisville, Louisville, KY;BioImaging Laboratory, Bioengineering Department, University of Louisville, Louisville, KY;Diagnostic Radiology Department, University of Louisville, Louisville, KY;Department of Computer Science, University of Auckland, Auckland, New Zealand;Urology and Nephrology Department, University of Mansoura, Mansoura, Egypt;Department of Medicine, University of Louisville, Louisville, KY;BioImaging Laboratory, Bioengineering Department, University of Louisville, Louisville, KY;BioImaging Laboratory, Bioengineering Department, University of Louisville, Louisville, KY;BioImaging Laboratory, Bioengineering Department, University of Louisville, Louisville, KY;BioImaging Laboratory, Bioengineering Department, University of Louisville, Louisville, KY
Venue:
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part III
Year:
2011

Citing 8
Cited 0

Alignment by Maximization of Mutual Information

International Journal of Computer Vision
An Automated Segmentation Method of Kidney Using Statistical Information

MICCAI '02 Proceedings of the 5th International Conference on Medical Image Computing and Computer-Assisted Intervention-Part I
A New Approach for Model-Based Adaptive Region Growing in Medical Image Analysis

CAIP '01 Proceedings of the 9th International Conference on Computer Analysis of Images and Patterns
Deformable Contour Method: A Constrained Optimization Approach

International Journal of Computer Vision
A segmentation framework for abdominal organs from CT scans

Artificial Intelligence in Medicine
Shape-Appearance Guided Level-Set Deformable Model for Image Segmentation

ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Non-parametric iterative model constraint graph min-cut for automatic kidney segmentation

MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part III
Precise segmentation of multimodal images

IEEE Transactions on Image Processing

Quantified Score

Hi-index	0.00

Visualization

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.