Skip to content Skip to navigation
University of Warwick
  • Study
  • |
  • Research
  • |
  • Business
  • |
  • Alumni
  • |
  • News
  • |
  • About

University of Warwick
Publications service & WRAP

Highlight your research

  • WRAP
    • Home
    • Search WRAP
    • Browse by Warwick Author
    • Browse WRAP by Year
    • Browse WRAP by Subject
    • Browse WRAP by Department
    • Browse WRAP by Funder
    • Browse Theses by Department
  • Publications Service
    • Home
    • Search Publications Service
    • Browse by Warwick Author
    • Browse Publications service by Year
    • Browse Publications service by Subject
    • Browse Publications service by Department
    • Browse Publications service by Funder
  • Help & Advice
University of Warwick

The Library

  • Login
  • Admin

Weighted level set evolution based on local edge features for medical image segmentation

Tools
- Tools
+ Tools

Khadidos, Alaa, Sanchez Silva, Victor and Li, Chang-Tsun (2017) Weighted level set evolution based on local edge features for medical image segmentation. IEEE Transactions on Image Processing, 26 (4). pp. 1979-1991. doi:10.1109/TIP.2017.2666042

[img]
Preview
PDF
WRAP_Sanchez_TIP_v5.pdf - Accepted Version - Requires a PDF viewer.

Download (4054Kb) | Preview
Official URL: http://doi.org/10.1109/TIP.2017.2666042

Request Changes to record.

Abstract

Level set methods have been widely used to implement active contours for image segmentation applications due to their good boundary detection accuracy. In the context of medical image segmentation, weak edges and inhomogeneities remain important issues that may hinder the accuracy of any segmentation method based on active contours implemented using level set methods. This paper proposes a method based on active contours implemented using level set methods for segmentation of such medical images. The proposed method uses a level set evolution that is based on the minimization of an objective energy functional whose energy terms are weighted according to their relative importance in detecting boundaries. This relative importance is computed based on local edge features collected from the adjacent region located inside and outside of the evolving contour. The local edge features employed are the edge intensity and the degree of alignment between the image’s gradient vector flow field and the evolving contour’s normal. We evaluate the proposed method for segmentation of various regions in real MRI and CT slices, X-ray images, and ultra sound images. Evaluation results confirm the advantage of weighting energy forces using local edge features to reduce leakage. These results also show that the proposed method leads to more accurate boundary detection results than state-of-the-art edge-based level set segmentation methods, particularly around weak edges.

Item Type: Journal Article
Subjects: Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
R Medicine > RC Internal medicine
Divisions: Faculty of Science > Computer Science
Library of Congress Subject Headings (LCSH): Diagnostic imaging, Magnetic resonance imaging, Tomography , X ray medical
Journal or Publication Title: IEEE Transactions on Image Processing
Publisher: IEEE
ISSN: 1057-7149
Official Date: April 2017
Dates:
DateEvent
April 2017Published
8 February 2017Available
14 January 2017Accepted
Volume: 26
Number: 4
Page Range: pp. 1979-1991
DOI: 10.1109/TIP.2017.2666042
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Restricted or Subscription Access
Funder: Engineering and Physical Sciences Research Council (EPSRC), Marie Curie Integration Grant

Request changes or add full text files to a record

Repository staff actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics

twitter

Email us: wrap@warwick.ac.uk
Contact Details
About Us