Liver segmentation from registered multiphase CT data sets with EM clustering and GVF level set

Tao, Guozhi and Singh, Ashish and Bidaut, Luc (2010) Liver segmentation from registered multiphase CT data sets with EM clustering and GVF level set. In: Medical Imaging 2010: Image Processing, 14 - 16 February 2010, San Diego, California, USA.

Full content URL: https://www.scopus.com/inward/record.uri?eid=2-s2....

Full text not available from this repository.

Item Type:Conference or Workshop contribution (Paper)
Item Status:Live Archive

Abstract

In this study, clinically produced multiphase CT volumetric data sets (pre-contrast, arterial and venous enhanced phase) are drawn upon to transcend the intrinsic limitations of single phase data sets for the robust and accurate segmentation of the liver in typically challenging cases. As an initial step, all other phase volumes are registered to either the arterial or venous phase volume by a symmetric nonlinear registration method using mutual information as similarity metric. Once registered, the multiphase CT volumes are pre-filtered to prepare for subsequent steps. Under the assumption that the intensity vectors of different organs follow the Gaussian Mixture model (GMM), expectation maximization (EM) is then used to classify the multiphase voxels into different clusters. The clusters for liver parenchyma, vessels and tumors are combined together and provide the initial liver mask that is used to generate initial zeros level set. Conversely, the voxels classified as non-liver will guide the speed image of the level sets in order to reduce leakage. Geodesic active contour level set using the gradient vector flow (GVF) derived from one of the enhanced phase volumes is then performed to further evolve the liver segmentation mask. Using EM clusters as the reference, the resulting liver mask is finally morphologically post-processed to add missing clusters and reduce leakage. The proposed method has been tested on the clinical data sets of ten patients with relatively complex and/or extensive liver cancer or metastases. A 95.8 dice similarity index when compared to expert manual segmentation demonstrates the high performance and the robustness of our proposed method - even for challenging cancer data sets - and confirms the potential of a more thorough computational exploitation of currently available clinical data sets. © 2010 Copyright SPIE - The International Society for Optical Engineering.

Additional Information:From Conference Volume 7623, Conference Code:83745
Keywords:cancer, GVF, level set, liver segmentation, multiple phase CT, Nonlinear registration, Diseases, Image segmentation, Imaging systems, Liver, Medical imaging, Volumetric analysis, Computerized tomography
Subjects:F Physical Sciences > F350 Medical Physics
Divisions:College of Science > School of Computer Science
ID Code:24131
Deposited On:07 Apr 2017 11:16

Repository Staff Only: item control page