Segmentation, Inference and Classification of Partially Overlapping Nanoparticles

Handle URI:
http://hdl.handle.net/10754/599569
Title:
Segmentation, Inference and Classification of Partially Overlapping Nanoparticles
Authors:
Chiwoo Park,; Huang, J. Z.; Ji, J. X.; Yu Ding,
Abstract:
This paper presents a method that enables automated morphology analysis of partially overlapping nanoparticles in electron micrographs. In the undertaking of morphology analysis, three tasks appear necessary: separate individual particles from an agglomerate of overlapping nano-objects; infer the particle's missing contours; and ultimately, classify the particles by shape based on their complete contours. Our specific method adopts a two-stage approach: the first stage executes the task of particle separation, and the second stage conducts simultaneously the tasks of contour inference and shape classification. For the first stage, a modified ultimate erosion process is developed for decomposing a mixture of particles into markers, and then, an edge-to-marker association method is proposed to identify the set of evidences that eventually delineate individual objects. We also provided theoretical justification regarding the separation capability of the first stage. In the second stage, the set of evidences become inputs to a Gaussian mixture model on B-splines, the solution of which leads to the joint learning of the missing contour and the particle shape. Using twelve real electron micrographs of overlapping nanoparticles, we compare the proposed method with seven state-of-the-art methods. The results show the superiority of the proposed method in terms of particle recognition rate.
Citation:
Chiwoo Park, Huang JZ, Ji JX, Yu Ding (2013) Segmentation, Inference and Classification of Partially Overlapping Nanoparticles. IEEE Trans Pattern Anal Mach Intell 35: 1–1. Available: http://dx.doi.org/10.1109/tpami.2012.163.
Publisher:
Institute of Electrical and Electronics Engineers (IEEE)
Journal:
IEEE Transactions on Pattern Analysis and Machine Intelligence
KAUST Grant Number:
KUS-CI-016-04
Issue Date:
Mar-2013
DOI:
10.1109/tpami.2012.163
PubMed ID:
22848127
Type:
Article
ISSN:
0162-8828; 2160-9292
Sponsors:
The authors would like to acknowledge the generous support from their sponsors. Ding and Park are partially supported by US National Science Foundation (NSF) grants CMMI-0348150, CMMI-1000088, and Texas Norman Hackerman Advanced Research Program grant 010366-0024-2007; Huang is partially supported by NSF grants DMS-0907170, DMS-1007618, and King Abdullah University of Science and Technology award KUS-CI-016-04; Ji is partially supported by NSF grant 0748180. The authors would also like to thank Dr. Hong Liang and Dr. Subrata Kundu in the Department of Mechanical Engineering at Texas A&M University for providing the electron micrographs of gold nanoparticles.
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Full metadata record

DC FieldValue Language
dc.contributor.authorChiwoo Park,en
dc.contributor.authorHuang, J. Z.en
dc.contributor.authorJi, J. X.en
dc.contributor.authorYu Ding,en
dc.date.accessioned2016-02-28T05:53:32Zen
dc.date.available2016-02-28T05:53:32Zen
dc.date.issued2013-03en
dc.identifier.citationChiwoo Park, Huang JZ, Ji JX, Yu Ding (2013) Segmentation, Inference and Classification of Partially Overlapping Nanoparticles. IEEE Trans Pattern Anal Mach Intell 35: 1–1. Available: http://dx.doi.org/10.1109/tpami.2012.163.en
dc.identifier.issn0162-8828en
dc.identifier.issn2160-9292en
dc.identifier.pmid22848127en
dc.identifier.doi10.1109/tpami.2012.163en
dc.identifier.urihttp://hdl.handle.net/10754/599569en
dc.description.abstractThis paper presents a method that enables automated morphology analysis of partially overlapping nanoparticles in electron micrographs. In the undertaking of morphology analysis, three tasks appear necessary: separate individual particles from an agglomerate of overlapping nano-objects; infer the particle's missing contours; and ultimately, classify the particles by shape based on their complete contours. Our specific method adopts a two-stage approach: the first stage executes the task of particle separation, and the second stage conducts simultaneously the tasks of contour inference and shape classification. For the first stage, a modified ultimate erosion process is developed for decomposing a mixture of particles into markers, and then, an edge-to-marker association method is proposed to identify the set of evidences that eventually delineate individual objects. We also provided theoretical justification regarding the separation capability of the first stage. In the second stage, the set of evidences become inputs to a Gaussian mixture model on B-splines, the solution of which leads to the joint learning of the missing contour and the particle shape. Using twelve real electron micrographs of overlapping nanoparticles, we compare the proposed method with seven state-of-the-art methods. The results show the superiority of the proposed method in terms of particle recognition rate.en
dc.description.sponsorshipThe authors would like to acknowledge the generous support from their sponsors. Ding and Park are partially supported by US National Science Foundation (NSF) grants CMMI-0348150, CMMI-1000088, and Texas Norman Hackerman Advanced Research Program grant 010366-0024-2007; Huang is partially supported by NSF grants DMS-0907170, DMS-1007618, and King Abdullah University of Science and Technology award KUS-CI-016-04; Ji is partially supported by NSF grant 0748180. The authors would also like to thank Dr. Hong Liang and Dr. Subrata Kundu in the Department of Mechanical Engineering at Texas A&M University for providing the electron micrographs of gold nanoparticles.en
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.titleSegmentation, Inference and Classification of Partially Overlapping Nanoparticlesen
dc.typeArticleen
dc.identifier.journalIEEE Transactions on Pattern Analysis and Machine Intelligenceen
dc.contributor.institutionDepartment of Industrial and Manufacturing Engineering, Florida A&M and Florida State University, Office: B319 COE, Tallahassee, FL 32310.en
kaust.grant.numberKUS-CI-016-04en
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