An adaptive multirate algorithm for acquisition of fluorescence microscopy data sets

We propose an algorithm for adaptive efficient acquisition of fluorescence microscopy data sets using a multirate (MR) approach. We simulate acquisition as part of a larger system for protein classification based on their subcellular location patterns and, thus, strive to maintain the achieved level...

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Bibliographic Details
Published in:IEEE transactions on image processing 2005-09, Vol.14 (9), p.1246-1253
Main Authors: Merryman, T.E., Kovacevic, J.
Format: Article
Language:English
Subjects:
Accuracy
Acquisitions & mergers
Algorithms
Data acquisition
Databases, Factual
Fluorescence
Image coding
Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods
Imaging, Three-Dimensional - methods
Information Storage and Retrieval - methods
Laser excitation
Microscopy
Microscopy, Confocal - methods
Microscopy, Fluorescence - methods
Photobleaching
Pixel
Proteins
Sample Size
Sampling methods
Signal Processing, Computer-Assisted
Studies
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Summary:We propose an algorithm for adaptive efficient acquisition of fluorescence microscopy data sets using a multirate (MR) approach. We simulate acquisition as part of a larger system for protein classification based on their subcellular location patterns and, thus, strive to maintain the achieved level of classification accuracy as much as possible. This problem is similar to image compression but unique due to additional restrictions, namely causality; we have access only to the information scanned up to that point. While we do want to acquire fewer samples with as low distortion as possible to achieve compression, our goal is to do so while affecting the overall classification accuracy as little as possible. We achieve this by using an adaptive MR scanning scheme which samples the regions of the image area that hold the most pertinent information. Our results show that we can achieve significant compression which we can then use to acquire faster or to increase space resolution of our data set, all while minimally affecting the classification accuracy of the entire system.
ISSN:1057-7149
1941-0042
DOI:10.1109/TIP.2005.855861