Development and testing of a novel image analysis algorithm for descriptive evaluation of shape change of a shrinkable soft material

Abstract Soft material can undergo non-uniform deformation or change of shape upon processing. Identifying shape and its change is nevertheless not straightforward. In this study, novel image-based algorithm that can be used to identify shapes of input images and at the same time classify non-unifor...

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Autores principales:	Pinpinat Stienkijumpai, Maturada Jinorose, Sakamon Devahastin
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/c09507f3caf94b2b8da5adbe660091d6
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spelling	oai:doaj.org-article:c09507f3caf94b2b8da5adbe660091d62021-12-02T17:23:39ZDevelopment and testing of a novel image analysis algorithm for descriptive evaluation of shape change of a shrinkable soft material10.1038/s41598-021-97141-62045-2322https://doaj.org/article/c09507f3caf94b2b8da5adbe660091d62021-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-97141-6https://doaj.org/toc/2045-2322Abstract Soft material can undergo non-uniform deformation or change of shape upon processing. Identifying shape and its change is nevertheless not straightforward. In this study, novel image-based algorithm that can be used to identify shapes of input images and at the same time classify non-uniform deformation into various patterns, i.e., swelling/shrinkage, horizontal and vertical elongations/contractions as well as convexity and concavity, is proposed. The algorithm was first tested with computer-generated images and later applied to agar cubes, which were used as model shrinkable soft material, undergoing drying at different temperatures. Shape parameters and shape-parameter based algorithm as well as convolutional neural networks (CNNs) either incorrectly identified some complicated shapes or could only identify the point where non-uniform deformation started to take place; CNNs lacked ability to describe non-uniform deformation evolution. Shape identification accuracy of the newly developed algorithm against computer-generated images was 65.88%, while those of the other tested algorithms ranged from 34.76 to 97.88%. However, when being applied to the deformation of agar cubes, the developed algorithm performed superiorly to the others. The proposed algorithm could both identify the shapes and describe their changes. The interpretation agreed well with that via visual observation.Pinpinat StienkijumpaiMaturada JinoroseSakamon DevahastinNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Pinpinat Stienkijumpai Maturada Jinorose Sakamon Devahastin Development and testing of a novel image analysis algorithm for descriptive evaluation of shape change of a shrinkable soft material
description	Abstract Soft material can undergo non-uniform deformation or change of shape upon processing. Identifying shape and its change is nevertheless not straightforward. In this study, novel image-based algorithm that can be used to identify shapes of input images and at the same time classify non-uniform deformation into various patterns, i.e., swelling/shrinkage, horizontal and vertical elongations/contractions as well as convexity and concavity, is proposed. The algorithm was first tested with computer-generated images and later applied to agar cubes, which were used as model shrinkable soft material, undergoing drying at different temperatures. Shape parameters and shape-parameter based algorithm as well as convolutional neural networks (CNNs) either incorrectly identified some complicated shapes or could only identify the point where non-uniform deformation started to take place; CNNs lacked ability to describe non-uniform deformation evolution. Shape identification accuracy of the newly developed algorithm against computer-generated images was 65.88%, while those of the other tested algorithms ranged from 34.76 to 97.88%. However, when being applied to the deformation of agar cubes, the developed algorithm performed superiorly to the others. The proposed algorithm could both identify the shapes and describe their changes. The interpretation agreed well with that via visual observation.
format	article
author	Pinpinat Stienkijumpai Maturada Jinorose Sakamon Devahastin
author_facet	Pinpinat Stienkijumpai Maturada Jinorose Sakamon Devahastin
author_sort	Pinpinat Stienkijumpai
title	Development and testing of a novel image analysis algorithm for descriptive evaluation of shape change of a shrinkable soft material
title_short	Development and testing of a novel image analysis algorithm for descriptive evaluation of shape change of a shrinkable soft material
title_full	Development and testing of a novel image analysis algorithm for descriptive evaluation of shape change of a shrinkable soft material
title_fullStr	Development and testing of a novel image analysis algorithm for descriptive evaluation of shape change of a shrinkable soft material
title_full_unstemmed	Development and testing of a novel image analysis algorithm for descriptive evaluation of shape change of a shrinkable soft material
title_sort	development and testing of a novel image analysis algorithm for descriptive evaluation of shape change of a shrinkable soft material
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/c09507f3caf94b2b8da5adbe660091d6
work_keys_str_mv	AT pinpinatstienkijumpai developmentandtestingofanovelimageanalysisalgorithmfordescriptiveevaluationofshapechangeofashrinkablesoftmaterial AT maturadajinorose developmentandtestingofanovelimageanalysisalgorithmfordescriptiveevaluationofshapechangeofashrinkablesoftmaterial AT sakamondevahastin developmentandtestingofanovelimageanalysisalgorithmfordescriptiveevaluationofshapechangeofashrinkablesoftmaterial
_version_	1718380993058963456

Development and testing of a novel image analysis algorithm for descriptive evaluation of shape change of a shrinkable soft material

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