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...

Ausführliche Beschreibung

Bibliographische Detailangaben
Veröffentlicht in:IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. - 1992. - 14(2005), 9 vom: 01. Sept., Seite 1246-53
1. Verfasser: Merryman, Thomas E (VerfasserIn)
Weitere Verfasser: Kovacević, Jelena
Format: Aufsatz
Sprache:English
Veröffentlicht: 2005
Zugriff auf das übergeordnete Werk:IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Schlagworte:Comparative Study Evaluation Study Journal Article Research Support, U.S. Gov't, Non-P.H.S.
Beschreibung
Zusammenfassung: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 aquire faster or to increase space resolution of our data set, all while minimally affecting the classification accuracy of the entire system
Beschreibung:Date Completed 27.10.2005
Date Revised 10.12.2019
published: Print
Citation Status MEDLINE
ISSN:1941-0042