Active contours with weighted external forces for medical image segmentation

Alaa Khadidos, Victor Sanchez, Chang-Tsun Li

Research output: Book chapter/Published conference paperConference paper

35 Downloads (Pure)

Abstract

Parametric active contours have been widely used for image segmentation. However,high noise levels and weak edges are the most acute issues that hinder their performance,particularly in medical images. In order to overcome these issues, we propose an external force that weights the gradient vector flow (GVF) field and balloon forces according to local image features. We also propose a mechanism to automatically terminate the contour’s deformation. Evaluation results on real MRI and CT slices show that the proposed approach attains higher segmentation accuracy than snakes using traditional external forces, while allowing initialization using a limited number of selected points.
Original languageEnglish
Title of host publicationMedical Image Understanding and Analysis 2014
Subtitle of host publicationProceedings of the 18th conference on Medical Image Understanding and Analysis
Place of PublicationUnited Kingdom
PublisherBritish Machine Vision Association and Society for Pattern Recognition
Pages149-154
Number of pages6
Publication statusPublished - 2014
Event18th Conference on Medical Image Understanding and Analysis 2014 - Royal Holloway, Egham, United Kingdom
Duration: 09 Jul 201411 Jul 2014
https://www.city.ac.uk/medical-image-understanding-and-analysis-2014

Conference

Conference18th Conference on Medical Image Understanding and Analysis 2014
CountryUnited Kingdom
CityEgham
Period09/07/1411/07/14
Internet address

Fingerprint Dive into the research topics of 'Active contours with weighted external forces for medical image segmentation'. Together they form a unique fingerprint.

  • Cite this

    Khadidos, A., Sanchez, V., & Li, C-T. (2014). Active contours with weighted external forces for medical image segmentation. In Medical Image Understanding and Analysis 2014: Proceedings of the 18th conference on Medical Image Understanding and Analysis (pp. 149-154). British Machine Vision Association and Society for Pattern Recognition.