Quantifying challenging images of fiber-like structures

Alessandro Giusti, Jonathan Masci, Paola M V Rancoita

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We present a practical, parameter-free, general computational-statistical technique for quantitative analysis of 2D images representing fiber-like structures (vessels, neurons, elongated objects, cell boundaries..), which is a common task in many experimental biomedicine scenarios. Our approach does not require segmentation or tracing of fibers; instead, it relies on a learned detector of intersections between fibers and arbitrary segments. The detector's probabilistic outputs are used to compute an estimate of the density of fibers and of its uncertainty; the latter accounts for several factors, including the intrinsic difficulty of the problem, i.e. the inaccuracy of the detector. After few minutes of training by the user, the procedure performs well in a variety of challenging scenarios, and compares favorably even with problem-specific algorithms.

Original languageEnglish
Title of host publication2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings
Pages1163-1166
Number of pages4
DOIs
Publication statusPublished - 2013
Event2013 20th IEEE International Conference on Image Processing, ICIP 2013 - Melbourne, VIC, Australia
Duration: Sep 15 2013Sep 18 2013

Other

Other2013 20th IEEE International Conference on Image Processing, ICIP 2013
Country/TerritoryAustralia
CityMelbourne, VIC
Period9/15/139/18/13

Keywords

  • Fibre-like structures
  • Medical Image Quantification
  • Segmentation

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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