Academic Journal
Machine learning-based multidomain processing for texture-based image segmentation and analysis.
| Title: | Machine learning-based multidomain processing for texture-based image segmentation and analysis. |
|---|---|
| Authors: | Borodinov, Nikolay, Tsai, Wan-Yu, Korolkov, Vladimir V., Balke, Nina, Kalinin, Sergei V., Ovchinnikova, Olga S. |
| Source: | Applied Physics Letters; 1/27/2020, Vol. 116 Issue 4, p1-5, 5p, 3 Color Photographs, 1 Graph |
| Subject Terms: | Image segmentation, Image processing, Image analysis, Feature extraction, Atomic force microscopy, Molecular orientation |
| Abstract: | Atomic and molecular resolved atomic force microscopy (AFM) images offer unique insights into materials' properties such as local ordering, molecular orientation, and topological defects, which can be used to pinpoint physical and chemical interactions occurring at the surface. Utilizing machine learning for extracting underlying physical parameters increases the throughput of AFM data processing and eliminates inconsistencies intrinsic to manual image analysis, thus enabling the creation of reliable frameworks for qualitative and quantitative evaluation of experimental data. Here, we present a robust and scalable approach to the segmentation of AFM images based on flexible pre-selected classification criteria. The usage of supervised learning and feature extraction allows us to retain the consideration of specific problem-dependent features (such as types of periodical structures observed in the images and the associated numerical parameters: spacing, orientation, etc.). We highlight the applicability of this approach for the segmentation of molecular resolved AFM images based on the crystal orientation of the observed domains, automated selection of boundaries, and collection of relevant statistics. Overall, we outline a general strategy for machine learning-enabled analysis of nanoscale systems exhibiting periodic order that could be applied to any analytical imaging technique. [ABSTRACT FROM AUTHOR] |
| Copyright of Applied Physics Letters is the property of American Institute of Physics and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Complementary Index |
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| Items | – Name: Title Label: Title Group: Ti Data: Machine learning-based multidomain processing for texture-based image segmentation and analysis. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Borodinov%2C+Nikolay%22">Borodinov, Nikolay</searchLink><br /><searchLink fieldCode="AR" term="%22Tsai%2C+Wan-Yu%22">Tsai, Wan-Yu</searchLink><br /><searchLink fieldCode="AR" term="%22Korolkov%2C+Vladimir+V%2E%22">Korolkov, Vladimir V.</searchLink><br /><searchLink fieldCode="AR" term="%22Balke%2C+Nina%22">Balke, Nina</searchLink><br /><searchLink fieldCode="AR" term="%22Kalinin%2C+Sergei+V%2E%22">Kalinin, Sergei V.</searchLink><br /><searchLink fieldCode="AR" term="%22Ovchinnikova%2C+Olga+S%2E%22">Ovchinnikova, Olga S.</searchLink> – Name: TitleSource Label: Source Group: Src Data: Applied Physics Letters; 1/27/2020, Vol. 116 Issue 4, p1-5, 5p, 3 Color Photographs, 1 Graph – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Image+segmentation%22">Image segmentation</searchLink><br /><searchLink fieldCode="DE" term="%22Image+processing%22">Image processing</searchLink><br /><searchLink fieldCode="DE" term="%22Image+analysis%22">Image analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+extraction%22">Feature extraction</searchLink><br /><searchLink fieldCode="DE" term="%22Atomic+force+microscopy%22">Atomic force microscopy</searchLink><br /><searchLink fieldCode="DE" term="%22Molecular+orientation%22">Molecular orientation</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Atomic and molecular resolved atomic force microscopy (AFM) images offer unique insights into materials' properties such as local ordering, molecular orientation, and topological defects, which can be used to pinpoint physical and chemical interactions occurring at the surface. Utilizing machine learning for extracting underlying physical parameters increases the throughput of AFM data processing and eliminates inconsistencies intrinsic to manual image analysis, thus enabling the creation of reliable frameworks for qualitative and quantitative evaluation of experimental data. Here, we present a robust and scalable approach to the segmentation of AFM images based on flexible pre-selected classification criteria. The usage of supervised learning and feature extraction allows us to retain the consideration of specific problem-dependent features (such as types of periodical structures observed in the images and the associated numerical parameters: spacing, orientation, etc.). We highlight the applicability of this approach for the segmentation of molecular resolved AFM images based on the crystal orientation of the observed domains, automated selection of boundaries, and collection of relevant statistics. Overall, we outline a general strategy for machine learning-enabled analysis of nanoscale systems exhibiting periodic order that could be applied to any analytical imaging technique. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Applied Physics Letters is the property of American Institute of Physics and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1063/1.5135328 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 5 StartPage: 1 Subjects: – SubjectFull: Image segmentation Type: general – SubjectFull: Image processing Type: general – SubjectFull: Image analysis Type: general – SubjectFull: Feature extraction Type: general – SubjectFull: Atomic force microscopy Type: general – SubjectFull: Molecular orientation Type: general Titles: – TitleFull: Machine learning-based multidomain processing for texture-based image segmentation and analysis. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Borodinov, Nikolay – PersonEntity: Name: NameFull: Tsai, Wan-Yu – PersonEntity: Name: NameFull: Korolkov, Vladimir V. – PersonEntity: Name: NameFull: Balke, Nina – PersonEntity: Name: NameFull: Kalinin, Sergei V. – PersonEntity: Name: NameFull: Ovchinnikova, Olga S. IsPartOfRelationships: – BibEntity: Dates: – D: 27 M: 01 Text: 1/27/2020 Type: published Y: 2020 Identifiers: – Type: issn-print Value: 00036951 Numbering: – Type: volume Value: 116 – Type: issue Value: 4 Titles: – TitleFull: Applied Physics Letters Type: main |
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