DocumentCode :
3580022
Title :
A new statistical and Dirichlet integral framework applied to liver segmentation from volumetric CT images
Author :
Changyang Li ; Ang Li ; Xiuying Wang ; Dagan Feng ; Eberl, Stefan ; Fulham, Michael
Author_Institution :
Biomedicai & Multimedia Inf. Technol. (BMIT) Res. Group, Univ. of Sydney, Sydney, NSW, Australia
fYear :
2014
Firstpage :
642
Lastpage :
647
Abstract :
Accurate liver segmentation from computed tomography (CT) images is problematic due to non-uniform density, weak boundaries and because there may be multiple liver tumors that have heterogeneous intensities in region(s) of interest (ROIs). So we propose a generalized energy framework that harnesses the statistical intensity approximation with image data on graphs. Our statistical energy term takes advantage of the mixture-of-mixtures Gaussian model to approximate the probability density distribution of the liver and background to better differentiate between the two. The probability density estimation can be combined with the spatial cohesion of the graph-based Dirichlet integral by using graph calculus. Matrix decomposition and differentiation are used to minimize our proposed energy functional. We tested our approach on 20 public high-contrast CT images with single and multiple liver tumors. Our method had an average dice similarity coefficient (DSC) of 93.75±1.29%, an average false positive (FP) rate of 9.43±3.52% and an average false negative (FN) rate of 3.48±1.48%. Our method outperformed the benchmark graph-based Random Walker algorithm (average DSC=81.97±4.09%, average FP rate 34.10±10.53%, and average FN rate 7.10±4.35%).
Keywords :
Gaussian processes; computerised tomography; graph theory; image segmentation; liver; matrix decomposition; medical image processing; mixture models; statistical distributions; tumours; DSC; Dirichlet integral framework; ROI; average dice similarity coefficient; computed tomography images; generalized energy framework; graph calculus; graph-based Dirichlet integral; graph-based random walker algorithm; image data; liver segmentation; liver tumors; matrix decomposition; matrix differentiation; mixture-of-mixtures Gaussian model; probability density distribution; region of interest; statistical energy term; statistical framework; statistical intensity approximation; volumetric CT images; Accuracy; Computed tomography; Estimation; Image edge detection; Image segmentation; Liver; Probability; Dirichlet integral; Image segmentation; energy minimization; mixture-of-mixtures Gaussian;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Control Automation Robotics & Vision (ICARCV), 2014 13th International Conference on
Type :
conf
DOI :
10.1109/ICARCV.2014.7064379
Filename :
7064379
Link To Document :
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