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Affiliation

Plant Organelle Shape and Dynamics Lab, Institute of Plant Physiology, Faculty of Natural Sciences I, Martin Luther University Halle-Wittenberg, Weinbergweg 10, 06120 Halle (Saale), Germany

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1 Papers

Quantification of Stromule Frequencies in Microscope Images of Plastids Combining Ridge Detection and Geometric Criteria

Topics: Fluorescent Microscopy; Quantitative Bioimaging; Image Processing Methods

Authors: Birgit Möller ¹ and Martin Schattat ²

Affiliations: ¹ Pattern Recognition and Bioinformatics, Institute of Computer Science, Faculty of Natural Sciences III, Martin Luther University Halle-Wittenberg, Von-Seckendorff-Platz 1, 06120 Halle (Saale), Germany , ² Plant Organelle Shape and Dynamics Lab, Institute of Plant Physiology, Faculty of Natural Sciences I, Martin Luther University Halle-Wittenberg, Weinbergweg 10, 06120 Halle (Saale), Germany

Digital Object Identifier: 10.5220/0007390300380048

Proceedings of BIOIMAGING 2019/BIOSTEC, ISBN: 978-989-758-353-7

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Plastids are involved in many fundamental biochemical pathways in plants. They can produce tubular membrane out-folds from their surface. These so-called stromules have initially been described over a century ago, but their functional role is still elusive. To identify cellular processes or genetic elements underlying stromule formation screens of large populations of mutant plants or plants under different treatments are carried out and stromule frequencies are extracted. Due to a lack of automatic methods, however, this quantification is usually done manually rendering this step a main bottleneck in stromule research. Here, we present a new approach for quantification of stromule frequencies. Plastids are extracted from microscope images using local wavelet analysis over multiple scales combined with statistical hypothesis testing to resolve competing detections from different scales. Subsequently, for each plastid region evidence for the existence of stromules in its vicinity is investigated applying ridge detection techniques and geometric criteria. Experimental results prove that our approach is suitable to properly identify stromules. Even in microscopy images with a high noise level and distracting signals extracted stromule counts are comparable to those of biological experts.