Anisotropic Gradient based Filtering for Object Segmentation in Medical Images

Ana João, Alberto Gambaruto, Adélia Sequeira

Research output: Contribution to journalArticle (Academic Journal)

Abstract

A four-step approach for image filtering and object segmentation is explored. The key steps are: i) a low-pass digital differentiator is used to compute the image gradient (vector) field; ii) the regularised anisotropic diffusion method is used to filter this vector field; iii) the modified image is reconstructed from the filtered gradient field as a least-squares best fit; iv) object segmentation is performed on the reconstructed image. The advantages of this approach is the easier identification of noise in the gradient field, and consequently the image filtering can be more effective. The least-squares fit allows for non-local adjustment to the image to improve overall smoothness while enhancing object contrast. Importantly one can also ensure that relevant feature boundary locations are preserved, while appearing smoother due to the filtering. The proposed filtering methodology is compared to image filtering applied directly to the image intensity. A set of challenging medical images of mammography exams and confocal microscopy experiments are used as numerical tests.

Structured keywords

  • Mathematics and Computational Biology
  • non-linear anisotropic filtering
  • fractional derivatives
  • object segmentation
  • regularised backward and forward anisotropic diffusion (RBAF)
  • image reconstruction from gradient field

Keywords

  • non-linear anisotropic filtering
  • fractional derivatives
  • object segmentation
  • regularised backward and forward anisotropic diffusion (RBAF)
  • image reconstruction from gradient field

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