{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Basak AK"],"funding":["Uniwersytet Jagielloński w Krakowie","Fundacja na rzecz Nauki Polskiej","Narodowym Centrum Nauki","Narodowe Centrum Badań i Rozwoju"],"pagination":["109"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC8549183"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["17(1)"],"pubmed_abstract":["<h4>Background</h4>Cellular components are controlled by genetic and physiological factors that define their shape and size. However, quantitively capturing the morphological characteristics and movement of cellular organelles from micrograph images is challenging, because the analysis deals with complexities of images that frequently lead to inaccuracy in the estimation of the features. Here we show a unique quantitative method to overcome biases and inaccuracy of biological samples from confocal micrographs.<h4>Results</h4>We generated 2D images of cell walls and spindle-shaped cellular organelles, namely ER bodies, with a maximum contrast projection of 3D confocal fluorescent microscope images. The projected images were further processed and segmented by adaptive thresholding of the flu"],"journal":["Plant methods"],"pubmed_title":["Texture feature extraction from microscope images enables a robust estimation of ER body phenotype in Arabidopsis."],"pmcid":["PMC8549183"],"funding_grant_id":["TEAM/2017-4/41","WND-POWR 03.02.00-00-I021/16","UMO-2016/23/B/NA1/01847"],"pubmed_authors":["Mirzaei M","Basak AK","Strzalka K","Yamada K"],"additional_accession":[]},"is_claimable":false,"name":"Texture feature extraction from microscope images enables a robust estimation of ER body phenotype in Arabidopsis.","description":"<h4>Background</h4>Cellular components are controlled by genetic and physiological factors that define their shape and size. However, quantitively capturing the morphological characteristics and movement of cellular organelles from micrograph images is challenging, because the analysis deals with complexities of images that frequently lead to inaccuracy in the estimation of the features. Here we show a unique quantitative method to overcome biases and inaccuracy of biological samples from confocal micrographs.<h4>Results</h4>We generated 2D images of cell walls and spindle-shaped cellular organelles, namely ER bodies, with a maximum contrast projection of 3D confocal fluorescent microscope images. The projected images were further processed and segmented by adaptive thresholding of the flu","dates":{"release":"2021-01-01T00:00:00Z","publication":"2021 Oct","modification":"2025-04-04T13:50:53.557Z","creation":"2025-04-04T13:50:53.557Z"},"accession":"S-EPMC8549183","cross_references":{"pubmed":["34702318"],"doi":["10.1186/s13007-021-00810-w"]}}