Full-body CT segmentation using 3D extension of two graph-based methods: a feasibility study

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    5 Citations (Scopus)

    Abstract

    The paper studies the feasibility of using 3D extensions of two state-of-the-art segmentation techniques, the Statistical Region Merging (SRM) method and the Efficient Graph-based Segmentation (EGS) technique, for automatic anatomy segmentation on clinical 3D CT images. The proposed methods are tested on a dataset of 55 images. The test is for segmentation of eight representative tissues (lungs, stomach, liver, heart, kidneys, spleen, bones and the spinal cord) which are vital for accurate calculation of radiation doses. The results are evaluated using the Dice index, the Hausdorff distance and the H t index, a measure of border error with tolerance t pixels addressing the uncertainty in the ground truth. The outcome shows that the 3D-SRM method outperforms 3D-EGS and has a great potential to become the method of choice for segmentation of full-body CT images. Using 3D-SRM, the average Dice index, the Hausdorff distance across the 8 tissues, and the H 2 were 0.89, 12.5 mm and 0.93, respectively.

    Original languageEnglish
    Pages43-50
    Number of pages8
    DOIs
    Publication statusPublished - 8 Aug 2012
    EventSignal Processing, Pattern Recognition and Applications (SPPRA 2012) -
    Duration: 18 Jun 2012 → …

    Conference

    ConferenceSignal Processing, Pattern Recognition and Applications (SPPRA 2012)
    Period18/06/12 → …

    Keywords

    • Efficient graph-based segmentation
    • Full-body CT
    • Image segmentation
    • Statistical region merging
    • Voxel model

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  • Cite this

    Bajger, M., Lee, G., & Caon, M. (2012). Full-body CT segmentation using 3D extension of two graph-based methods: a feasibility study. 43-50. Paper presented at Signal Processing, Pattern Recognition and Applications (SPPRA 2012), . https://doi.org/10.2316/P.2012.778-050