On Detection of Faint Edges in Noisy Images

Nati Ofir, Meirav Galun, Sharon Alpert, Achi Brandt, Boaz Nadler, Ronen Basri

Research output: Contribution to journalArticlepeer-review

Abstract

A fundamental question for edge detection in noisy images is how faint can an edge be and still be detected. In this paper we offer a formalism to study this question and subsequently introduce computationally efficient multiscale edge detection algorithms designed to detect faint edges in noisy images. In our formalism we view edge detection as a search in a discrete, though potentially large, set of feasible curves. First, we derive approximate expressions for the detection threshold as a function of curve length and the complexity of the search space. We then present two edge detection algorithms, one for straight edges, and the second for curved ones. Both algorithms efficiently search for edges in a large set of candidates by hierarchically constructing difference filters that match the curves traced by the sought edges. We demonstrate the utility of our algorithms in both simulations and applications involving challenging real images. Finally, based on these principles, we develop an algorithm for fiber detection and enhancement. We exemplify its utility to reveal and enhance nerve axons in light microscopy images.

Original languageEnglish
Article number8607091
Pages (from-to)894-908
Number of pages15
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Volume42
Issue number4
Early online date10 Jan 2019
DOIs
StatePublished - 1 Apr 2020

Keywords

  • Edge detection
  • fiber enhancement
  • low signal-to-noise ratio
  • microscopy images
  • multiple hypothesis tests
  • multiscale methods

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence
  • Applied Mathematics
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics

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