Characterizing three-dimensional open cell structures without segmentation

被引:0
|
作者
Nurre, Joseph H. [1 ]
Dufresne, Thomas E. [1 ]
Gideon, John H. [2 ]
机构
[1] Procter & Gamble Co, Surface Imaging & Microscopy Dept, 8700 Mason Montgomery Rd, Mason, OH 45040 USA
[2] Univ Michigan, Dept Comp Sci & Engn, Ann Arbor, MI 48109 USA
关键词
Computed tomography; Foam; 3D Image Geometry; 2D Image Geometry; Volume measurements; Watershed; Object segmentation; Euclidian Distance Map;
D O I
10.1117/12.2304394
中图分类号
O43 [光学];
学科分类号
070207 ; 0803 ;
摘要
Foam cells, particle conglomerates, biological tissue slices and colloidal suspensions are just a few examples of collections that create an image with multiple touching or overlapping regions. The characterization of the open cell size of such a continuous structure is tedious and computationally intensive for large 3D data sets. Typically, it is accomplished by segmenting the cells with a watershed technique and aggregating the statistics of all regions found. This paper provides the mathematical foundation for a newly discovered relationship between the average pixel value of a Euclidean Distance Map (EDM) and the radius of a conic section. The implementation of this relationship allows for a computationally simple and accurate characterization of the aggregate diameter associated with these open cell structures without segmentation.
引用
收藏
页数:6
相关论文
共 50 条
  • [31] Three-dimensional medical image segmentation and visualization
    Tang, Guo
    Zhao, Xiaodong
    Wang, Yuanmei
    Jisuanji Xuebao/Chinese Journal of Computers, 21 (03): : 204 - 209
  • [32] Unsupervised segmentation of three-dimensional brain images
    Ruan, S
    Fadili, J
    Xue, JH
    Bloyet, D
    15TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOL 3, PROCEEDINGS: IMAGE, SPEECH AND SIGNAL PROCESSING, 2000, : 405 - 408
  • [33] Efficient, interactive, and three-dimensional segmentation of cell nuclei in thick tissue sections
    Lockett, SJ
    Sudar, D
    Thompson, CT
    Pinkel, D
    Gray, JW
    CYTOMETRY, 1998, 31 (04): : 275 - 286
  • [34] A Three-Dimensional Index for Characterizing Crop Water Stress
    Torrion, Jessica A.
    Maas, Stephan J.
    Guo, Wenxuan
    Bordovsky, James P.
    Cranmer, Andy M.
    REMOTE SENSING, 2014, 6 (05) : 4025 - 4042
  • [35] Characterizing faculty motivation to implement three-dimensional learning
    Nelson P.C.
    Matz R.L.
    Bain K.
    Fata-Hartley C.L.
    Cooper M.M.
    Disciplinary and Interdisciplinary Science Education Research, 5 (1)
  • [36] A three-dimensional thalamocortical dataset for characterizing brain heterogeneity
    Judy A. Prasad
    Aishwarya H. Balwani
    Erik C. Johnson
    Joseph D. Miano
    Vandana Sampathkumar
    Vincent De Andrade
    Kamel Fezzaa
    Ming Du
    Rafael Vescovi
    Chris Jacobsen
    Konrad P. Kording
    Doga Gürsoy
    William Gray Roncal
    Narayanan Kasthuri
    Eva L. Dyer
    Scientific Data, 7
  • [37] Characterizing the Three-Dimensional Flow in Partially Vegetated Channels
    Villota, S. Unigarro
    Ghisalberti, M.
    Philip, J.
    Branson, P.
    WATER RESOURCES RESEARCH, 2023, 59 (01)
  • [38] Three-dimensional sampling method for characterizing ant mounds
    Vogt, James T.
    FLORIDA ENTOMOLOGIST, 2007, 90 (03) : 553 - 558
  • [39] A three-dimensional thalamocortical dataset for characterizing brain heterogeneity
    Prasad, Judy A.
    Balwani, Aishwarya H.
    Johnson, Erik C.
    Miano, Joseph D.
    Sampathkumar, Vandana
    De Andrade, Vincent
    Fezzaa, Kamel
    Du, Ming
    Vescovi, Rafael
    Jacobsen, Chris
    Kording, Konrad P.
    Guersoy, Doga
    Roncal, William Gray
    Kasthuri, Narayanan
    Dyer, Eva L.
    SCIENTIFIC DATA, 2020, 7 (01)
  • [40] Methods for reconstruction of three-dimensional structures
    Antos, K
    Jezek, B
    Homola, A
    Kubinova, L
    Felkel, P
    ACTA VETERINARIA BRNO, 1996, 65 (04) : 237 - 245