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An active contour model based on local fitted images for image segmentation.

Lei Wang1,2, Yan Chang1, Hui Wang1

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Area of Science:

  • Computer Vision
  • Image Processing
  • Computational Imaging

Background:

  • Active contour models are widely used for image segmentation.
  • Intensity inhomogeneity can limit the performance of traditional active contour models.

Purpose of the Study:

  • To propose a novel region-based active contour model to overcome limitations caused by intensity inhomogeneity.
  • To enhance the accuracy of image segmentation.

Main Methods:

  • A novel local hybrid image fitting energy is constructed.
  • The model is minimized within a variational level set framework.
  • The evolving contour curves are guided towards desired boundaries.

Main Results:

  • The proposed model was evaluated against several typical active contour models.
  • Segmentation of synthetic and real images with varying intensity characteristics was performed.
  • Experimental results demonstrated superior accuracy compared to existing models.

Conclusions:

  • The novel region-based active contour model effectively addresses intensity inhomogeneity.
  • The proposed model achieves higher accuracy in image segmentation tasks.