An adaptive approach to centerline extraction for CT colonography using MAP-EM segmentation and distance field

Hao Peng; Lihong C. Li; Huafeng Wang; Hao Han; Perry J. Pickhardt; Zhengrong Liang

doi:10.1117/12.2043956

24 March 2014 An adaptive approach to centerline extraction for CT colonography using MAP-EM segmentation and distance field

Hao Peng, Lihong C. Li, Huafeng Wang, Hao Han, Perry J. Pickhardt, Zhengrong Liang

Author Affiliations +

Proceedings Volume 9035, Medical Imaging 2014: Computer-Aided Diagnosis; 903510 (2014) https://doi.org/10.1117/12.2043956
Event: SPIE Medical Imaging, 2014, San Diego, California, United States

Abstract

In this paper, we present an adaptive approach for fully automatic centerline extraction and small intestine removal based on partial volume (PV) image segmentation and distance field modeling. Computed tomographic colonography (CTC) volume image is first segmented for the colon wall mucosa layer, which represents the PV effect around the colon wall. Then centerline extraction is performed in the presence of colon collapse and small intestine touch by the use of distance field within the segmented PV mucosa layer, where centerline breakings due to collapse are recovered and centerline branches due to small intestine tough are removed. Experimental results from 24 patient CTC scans with small intestine touch rendered 100% removal of the touch, while only 16 out of the 24 could be done by the well-known isolated component method. Our voxel-by-voxel marking strategy in the automated procedure preserves the topology and validity of the colon structure. The marked inner and outer boundaries on cleansed colon are very close to those labeled by the experts. Experimental results demonstrated the robustness and efficiency of the presented adaptive approach for CTC utility.

Citation Download Citation

Hao Peng, Lihong C. Li, Huafeng Wang, Hao Han, Perry J. Pickhardt, and Zhengrong Liang "An adaptive approach to centerline extraction for CT colonography using MAP-EM segmentation and distance field", Proc. SPIE 9035, Medical Imaging 2014: Computer-Aided Diagnosis, 903510 (24 March 2014); https://doi.org/10.1117/12.2043956

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