Biomedical image time series registration with particle filtering

We propose a family of methods for biomedical image time series registration based on Particle filtering. The first method applies an intensity-based information-theoretic approach to calculate importance weights. An effective second group of methods use landmark-based approaches for the same purpos...

Full description

Saved in:
Bibliographic Details
Main Authors: Murat Yagci, A., Erdil, E., Argunsah, A. Ozgur, Unay, D., Cetin, M., Akarun, L., Gurgen, F.
Format: Conference Proceeding
Language:eng ; tur
Subjects:
Bayesian filtering
Biomedical image registration
Biomedical imaging
Image analysis
Image registration
Mutual information
Neural image analysis
Space exploration
Time series analysis
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:We propose a family of methods for biomedical image time series registration based on Particle filtering. The first method applies an intensity-based information-theoretic approach to calculate importance weights. An effective second group of methods use landmark-based approaches for the same purpose by automatically detecting intensity maxima or SIFT interest points from image time series. A brute-force search for the best alignment usually produces good results with proper cost functions, but becomes computationally expensive if the whole search space is explored. Hill climbing optimizations seek local optima. Particle filtering avoids local solutions by introducing randomness and sequentially updating the posterior distribution representing probable solutions. Thus, it can be more robust for the registration of image time series. We show promising preliminary results on dendrite image time series.
DOI:10.1109/SIU.2013.6531501