Anterior cruciate ligament segmentation from knee MR images using graph cuts with geometric and probabilistic shape constraints

Authors:
Hansang Lee;Helen Hong;Junmo Kim
Affiliations:
Department of Electrical Engineering, KAIST, South Korea;Department of Multimedia Engineering, Seoul Women's University, South Korea;Department of Electrical Engineering, KAIST, South Korea
Venue:
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part II
Year:
2012

Citing 5
Cited 0

Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations

Journal of Computational Physics
What Energy Functions Can Be Minimizedvia Graph Cuts?

IEEE Transactions on Pattern Analysis and Machine Intelligence
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision

IEEE Transactions on Pattern Analysis and Machine Intelligence
Interactive Graph Cut Based Segmentation with Shape Priors

CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Graph Cuts and Efficient N-D Image Segmentation

International Journal of Computer Vision

Quantified Score

Hi-index	0.00

Visualization

Abstract

Automatic segmentation of anterior cruciate ligament (ACL) is a challenging task due to its similar intensities with adjacent soft tissues, and inhomogeneity inside it in 3D knee magnetic resonance (MR) images. In this paper, an automatic ACL segmentation from 3D knee MR images using graph cuts is proposed. The proposed method consists of two steps: First, in the rough segmentation, adaptive thresholding using GMM fitting and ACL candidates extraction is performed to extract initial object and background candidates. Second, in the fine segmentation, iterative graph cut segmentation is incorporated with additional constraints including geometric and probabilistic shape costs to prevent the segmented ACL label from the leakage into adjacent soft tissues e.g. posterior cruciate ligament (PCL) and cartilage. In the experimental results, compared to the preceding work [1], the proposed method shows overall improved performances in sensitivity, specificity, and Dice similarity coefficient of 25%, 0.1%, and 29% for whole ACL, 34%, 0.5%, and 41% for major stem of ACL, respectively.