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    Segmentation, Inference and Classification of Partially Overlapping Nanoparticles

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    Type
    Article
    Authors
    Chiwoo Park,
    Huang, J. Z.
    Ji, J. X.
    Yu Ding,
    KAUST Grant Number
    KUS-CI-016-04
    Date
    2013-03
    Permanent link to this record
    http://hdl.handle.net/10754/599569
    
    Metadata
    Show full item record
    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.
    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.
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Journal
    IEEE Transactions on Pattern Analysis and Machine Intelligence
    DOI
    10.1109/tpami.2012.163
    PubMed ID
    22848127
    ae974a485f413a2113503eed53cd6c53
    10.1109/tpami.2012.163
    Scopus Count
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