Multi-organ segmentation of CT images using statistical region merging

    Research output: Contribution to conferencePaper

    8 Citations (Scopus)

    Abstract

    Segmentation is one of the key steps in the process of developing anatomical models for calculation of safe medical dose of radiation for children. This study explores the potential of the Statistical Region Merging segmentation technique for tissue segmentation in CT images. An analytical criterion allowing for an automatic tuning of the method is developed. The experiments are performed using a data set of 54 images from one patient, demonstrating the validity of the proposed criterion. The results are evaluated using the Jaccard index and a measure of border error with tolerance which addresses, application-dependant, acceptable error. The outcome shows that the technique has a great potential to become a method of choice for segmentation of CT images with an overall average boundary precison, for six representative tissues, equal to 0.937.

    Original languageEnglish
    Pages199-206
    Number of pages8
    DOIs
    Publication statusPublished - 16 Jul 2012
    EventNinth IASTED International Conference on Biomedical Engineering, BioMed 2012 -
    Duration: 15 Feb 2012 → …

    Conference

    ConferenceNinth IASTED International Conference on Biomedical Engineering, BioMed 2012
    Period15/02/12 → …

    Keywords

    • Image segmentation
    • Statistical region merging
    • Voxel model

    Fingerprint Dive into the research topics of 'Multi-organ segmentation of CT images using statistical region merging'. Together they form a unique fingerprint.

  • Cite this

    Lee, G., Bajger, M., & Caon, M. (2012). Multi-organ segmentation of CT images using statistical region merging. 199-206. Paper presented at Ninth IASTED International Conference on Biomedical Engineering, BioMed 2012, . https://doi.org/10.2316/P.2012.764-052