Joint volumetric extraction and enhancement of vasculature from low-SNR 3-D fluorescence microscopy images

Sepideh Almasi*, Ayal Ben-Zvi, Baptiste Lacoste, Chenghua Gu, Eric L. Miller, Xiaoyin Xu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

To simultaneously overcome the challenges imposed by the nature of optical imaging characterized by a range of artifacts including space-varying signal to noise ratio (SNR), scattered light, and non-uniform illumination, we developed a novel method that segments the 3-D vasculature directly from original fluorescence microscopy images eliminating the need for employing pre- and post-processing steps such as noise removal and segmentation refinement as used with the majority of segmentation techniques. Our method comprises two initialization and constrained recovery and enhancement stages. The initialization approach is fully automated using features derived from bi-scale statistical measures and produces seed points robust to non-uniform illumination, low SNR, and local structural variations. This algorithm achieves the goal of segmentation via design of an iterative approach that extracts the structure through voting of feature vectors formed by distance, local intensity gradient, and median measures. Qualitative and quantitative analysis of the experimental results obtained from synthetic and real data prove the efficacy of this method in comparison to the state-of-the-art enhancing-segmenting methods. The algorithmic simplicity, freedom from having a priori probabilistic information about the noise, and structural definition gives this algorithm a wide potential range of applications where i.e. structural complexity significantly complicates the segmentation problem.

Original languageAmerican English
Pages (from-to)710-718
Number of pages9
JournalPattern Recognition
Volume63
DOIs
StatePublished - 1 Mar 2017

Bibliographical note

Publisher Copyright:
© 2016 Elsevier Ltd

Keywords

  • Image segmentation
  • Non-uniform illumination
  • Tubular structures

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