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Segmentation of meristem cells by an automated opinion algorithm

Rojas, Oswaldo, Forero, Manuel G., Menendez, Jose M., Jones, Angharad, Dewitte, Walter and Murray, James A. H. 2020. Segmentation of meristem cells by an automated opinion algorithm. Journal of Applied Sciences 10 (23) , 8523. 10.39390/app10238523

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Abstract

Meristem cells are irregularly shaped and appear in confocal images as dark areas surrounded by bright ones. Images are characterized by regions of very low contrast and absolute loss of edges deeper into the meristem. Edges are blurred, discontinuous, sometimes indistinguishable, and the intensity level inside the cells is similar to the background of the image. Recently, a technique called Parametric Segmentation Tuning was introduced for the optimization of segmentation parameters in diatom images. This paper presents a PST-tuned automatic segmentation method of meristem cells in microscopy images based on mathematical morphology. The optimal parameters of the algorithm are found by means of an iterative process that compares the segmented images obtained by successive variations of the parameters. Then, an optimization function is used to determine which pair of successive images allows for the best segmentation. The technique was validated by comparing its results with those obtained by a level set algorithm and a balloon segmentation technique. The outcomes show that our methodology offers better results than two free available state-of-the-art alternatives, being superior in all cases studied, losing 9.09% of the cells in the worst situation, against 75.81 and 25.45 obtained in the level set and the balloon segmentation techniques, respectively. The optimization method can be employed to tune the parameters of other meristem segmentation methods.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Biosciences
Additional Information: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Publisher: Asian Network for Scientific Information (ANSINET)
ISSN: 1812-5654
Funders: BBSRC
Date of First Compliant Deposit: 7 December 2020
Date of Acceptance: 25 November 2020
Last Modified: 20 Jan 2021 13:14
URI: http://orca-mwe.cf.ac.uk/id/eprint/136842

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