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2D and 3D Vascular Structures Enhancement via Multiscale Fractional Anisotropy Tensor

  • Haifa F. Alhasson
  • Shuaa S. Alharbi
  • Boguslaw ObaraEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11134)

Abstract

The detection of vascular structures from noisy images is a fundamental process for extracting meaningful information in many applications. Most well-known vascular enhancing techniques often rely on Hessian-based filters. This paper investigates the feasibility and deficiencies of detecting curve-like structures using a Hessian matrix. The main contribution is a novel enhancement function, which overcomes the deficiencies of established methods. Our approach has been evaluated quantitatively and qualitatively using synthetic examples and a wide range of real 2D and 3D biomedical images. Compared with other existing approaches, the experimental results prove that our proposed approach achieves high-quality curvilinear structure enhancement.

Keywords

Curvilinear structures Image enhancement Enhancement filter Tensor representation Hessian matrix Diffusion tensor Fractional diffusion tensor FAT 

Supplementary material

478828_1_En_26_MOESM1_ESM.pdf (1.2 mb)
Supplementary material 1 (pdf 1255 KB)

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of Computer SciencesDurham UniversityDurhamUK
  2. 2.Computer CollegeQassim UniversityBuraidahKingdom of Saudi Arabia

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