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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/20539

Title: Fully automated muscle quality assessment by Gabor filtering of second harmonic generation images
Authors: Paesen, Rik
Smolders, Sophie
Manolo de Hoyos Vega, José
Op't Eijnde, Bert
Hansen, Dominique
Ameloot, Marcel
Issue Date: 2016
Abstract: Although structural changes on the sarcomere level of skeletal muscle are known to occur due to various pathologies, rigorous studies of the reduced sarcomere quality remain scarce. This can possibly be explained by the lack of an objective tool for analyzing and comparing sarcomere images across biological conditions. Recent developments in second harmonic generation (SHG) microscopy and increasing insight into the interpretation of sarcomere SHG intensity profiles have made SHG microscopy a valuable tool to study microstructural properties of sarcomeres. Typically, sarcomere integrity is analyzed by fitting a set of manually selected, one-dimensional SHG intensity profiles with a supramolecular SHG model. To circumvent this tedious manual selection step, we developed a fully automated image analysis procedure to map the sarcomere disorder for the entire image at once. The algorithm relies on a single-frequency wavelet-based Gabor approach and includes a newly developed normalization procedure allowing for unambiguous data interpretation. The method was validated by showing the correlation between the sarcomere disorder, quantified by the M-band size obtained from manually selected profiles, and the normalized Gabor value ranging from 0 to 1 for decreasing disorder. Finally, to elucidate the applicability of our newly developed protocol, Gabor analysis was used to study the effect of experimental autoimmune encephalomyelitis on the sarcomere regularity. We believe that the technique developed in this work holds great promise for high-throughput, unbiased, and automated image analysis to study sarcomere integrity by SHG microscopy.
Notes: Marcel Ameloot, Hasselt Univ, Biomed Res Inst, Agoralaan Bldg C, B-3590 Diepenbeek, Belgium. marcel.ameloot@uhasselt.be
URI: http://hdl.handle.net/1942/20539
DOI: 10.1117/1. JBO.21.2.026003
ISI #: 000371735000014
ISSN: 1083-3668
Category: A1
Type: Journal Contribution
Validation: ecoom, 2017
Appears in Collections: Research publications

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