Personal tools

Periodic Area-of-Motion characterization for Bio-Medical applications

From LRDE

Jump to: navigation, search

Abstract

Many bio-medical applications involve the analysis of se- quences for motion characterization. In this article, we con- sider 2D+t sequences where a particular motion (e.g. a blood flow) is associated with a specific area of the 2D image (e.g. an artery) but multiple motions may exist simultaneously in the same sequences (e.g. there may be several blood vessels present, each with their specific flow). The characterization of this type of motion typically involves first finding the areas where motion is present, followed by an analysis of these motions: speedregularity, frequency, etc. In this article, we propose a methodology called "area-of-motion characteriza- tion" suitable for simultaneously detecting and characterizing areas where motion is present in a sequence. We can then classify this motion into consistent areas using unsupervised learning and produce directly usable metrics for various ap- plications. We illustrate this methodology for the analysis of cilia motion on ex-vivo human samplesand we apply and validate the same methodology for blood flow analysis in fish embryo.

Documents

Bibtex (lrde.bib)

@InProceedings{	  puybareau.17.isbi,
  author	= {Elodie Puybareau and Hugues Talbot and Laurent Najman},
  title		= {Periodic Area-of-Motion characterization for Bio-Medical
		  applications},
  booktitle	= {Proceedings of the IEEE International Symposium on
		  Bio-Medical Imaging (ISBI)},
  year		= 2017,
  address	= {Melbourne, Australia},
  month		= apr,
  abstract	= {Many bio-medical applications involve the analysis of se-
		  quences for motion characterization. In this article, we
		  con- sider 2D+t sequences where a particular motion (e.g. a
		  blood flow) is associated with a specific area of the 2D
		  image (e.g. an artery) but multiple motions may exist
		  simultaneously in the same sequences (e.g. there may be
		  several blood vessels present, each with their specific
		  flow). The characterization of this type of motion
		  typically involves first finding the areas where motion is
		  present, followed by an analysis of these motions: speed,
		  regularity, frequency, etc. In this article, we propose a
		  methodology called "area-of-motion characteriza- tion"
		  suitable for simultaneously detecting and characterizing
		  areas where motion is present in a sequence. We can then
		  classify this motion into consistent areas using
		  unsupervised learning and produce directly usable metrics
		  for various ap- plications. We illustrate this methodology
		  for the analysis of cilia motion on ex-vivo human samples,
		  and we apply and validate the same methodology for blood
		  flow analysis in fish embryo.},
  note		= {To appear}
}