Unsupervised active contours driven by density distance and local fitting energy with applications to medical image segmentation

Kuo Kai Shyu, Van Truong Pham, Thi Thao Tran, Po Lei Lee

Research output: Contribution to journalArticlepeer-review

16 Scopus citations

Abstract

This study presents an efficient variational region-based active contour model for segmenting images without priori knowledge about the object or background. In order to handle intensity inhomogeneities and noise, we propose to integrate into the region-based local intensity model a global density distance inspired by the Bhattacharyya flow. The local term based on local information of segmented image allows the model to deal with bias field artifact, which arises in data acquisition processes. The global term, which is based on the density distance between the probability distribution functions of image intensity inside and outside the active contour, provides information for accurate segmentation, keeps the curve from spilling, and addresses noise in the image. Intensive 2D and 3D experiments on many imaging modalities of medical fields such as computed tomography, magnetic resonance imaging, and ultrasound images demonstrate the effectiveness of the model when dealing with images with blurred object boundary, intensity inhomogene-ities, and noise.

Original languageEnglish
Pages (from-to)1159-1175
Number of pages17
JournalMachine Vision and Applications
Volume23
Issue number6
DOIs
StatePublished - Nov 2012

Keywords

  • Active contour model
  • Image segmentation
  • Intensity inhomogeneity
  • Level set method
  • Variational method

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