Three-dimensional multi-scale line filter for segmentation and visualization of curvilinear structures in medical images

Yoshinobu Sato, Shin Nakajima, Nobuyuki Shiraga, Hideki Atsumi, Shigeyuki Yoshida, Thomas Koller, Guido Gerig, Ron Kikinis

    Research output: Contribution to journalArticlepeer-review

    Abstract

    This paper describes a method for the enhancement of curvilinear structures such as vessels and bronchi in three-dimensional (3-D) medical images. A 3-D line enhancement filter is developed with the aim of discriminating line structures from other structures and recovering line structures of various widths. The 3-D line filter is based on a combination of the eigenvalues of the 3-D Hessian matrix. Multi-scale integration is formulated by taking the maximum among single-scale filter responses, and its characteristics are examined to derive criteria for the selection of parameters in the formulation. The resultant multi-scale line-filtered images provide significantly improved segmentation and visualization of curvilinear structures. The usefulness of the method is demonstrated by the segmentation and visualization of brain vessels from magnetic resonance imaging (MRI) and magnetic resonance angiography (MRA), bronchi from a chest CT, and liver vessels (portal veins) from an abdominal CT.

    Original languageEnglish (US)
    Pages (from-to)143-168
    Number of pages26
    JournalMedical Image Analysis
    Volume2
    Issue number2
    DOIs
    StatePublished - 1998

    Keywords

    • 3-D image analysis
    • Line detection
    • Line measure
    • Multi-scale integration
    • Vessel enhancement

    ASJC Scopus subject areas

    • Radiological and Ultrasound Technology
    • Radiology Nuclear Medicine and imaging
    • Computer Vision and Pattern Recognition
    • Health Informatics
    • Computer Graphics and Computer-Aided Design

    Fingerprint

    Dive into the research topics of 'Three-dimensional multi-scale line filter for segmentation and visualization of curvilinear structures in medical images'. Together they form a unique fingerprint.

    Cite this