Advanced search
1 file | 1.03 MB

A novel dictionary based computer vision method for the detection of cell nuclei

(2013) PLOS ONE. 8(1).
Author
Organization
Abstract
Cell nuclei detection in fluorescent microscopic images is an important and time consuming task in a wide range of biological applications. Blur, clutter, bleed through and partial occlusion of nuclei make individual nuclei detection a challenging task for automated image analysis. This paper proposes a novel and robust detection method based on the active contour framework. Improvement over conventional approaches is achieved by exploiting prior knowledge of the nucleus shape in order to better detect individual nuclei. This prior knowledge is defined using a dictionary based approach which can be formulated as the optimization of a convex energy function. The proposed method shows accurate detection results for dense clusters of nuclei, for example, an F-measure (a measure for detection accuracy) of 0.96 for the detection of cell nuclei in peripheral blood mononuclear cells, compared to an F-measure of 0.90 achieved by state-of-the-art nuclei detection methods.
Keywords
SOFTWARE, EXTRACTION, SHAPE PRIOR, IMAGE SEGMENTATION, ACTIVE CONTOUR MODEL, MICROSCOPY, TRACKING, CIRCLES, GAS

Downloads

  • journal.pone.0054068.pdf
    • full text
    • |
    • open access
    • |
    • PDF
    • |
    • 1.03 MB

Citation

Please use this url to cite or link to this publication:

Chicago
De Vylder, Jonas, Jan Aelterman, Trees Lepez, Mado Vandewoestyne, Koen Douterloigne, Dieter Deforce, and Wilfried Philips. 2013. “A Novel Dictionary Based Computer Vision Method for the Detection of Cell Nuclei.” Plos One 8 (1).
APA
De Vylder, Jonas, Aelterman, J., Lepez, T., Vandewoestyne, M., Douterloigne, K., Deforce, D., & Philips, W. (2013). A novel dictionary based computer vision method for the detection of cell nuclei. PLOS ONE, 8(1).
Vancouver
1.
De Vylder J, Aelterman J, Lepez T, Vandewoestyne M, Douterloigne K, Deforce D, et al. A novel dictionary based computer vision method for the detection of cell nuclei. PLOS ONE. 2013;8(1).
MLA
De Vylder, Jonas, Jan Aelterman, Trees Lepez, et al. “A Novel Dictionary Based Computer Vision Method for the Detection of Cell Nuclei.” PLOS ONE 8.1 (2013): n. pag. Print.
@article{3109791,
  abstract     = {Cell nuclei detection in fluorescent microscopic images is an important and time consuming task in a wide range of biological applications. Blur, clutter, bleed through and partial occlusion of nuclei make individual nuclei detection a challenging task for automated image analysis. This paper proposes a novel and robust detection method based on the active contour framework. Improvement over conventional approaches is achieved by exploiting prior knowledge of the nucleus shape in order to better detect individual nuclei. This prior knowledge is defined using a dictionary based approach which can be formulated as the optimization of a convex energy function. The proposed method shows accurate detection results for dense clusters of nuclei, for example, an F-measure (a measure for detection accuracy) of 0.96 for the detection of cell nuclei in peripheral blood mononuclear cells, compared to an F-measure of 0.90 achieved by state-of-the-art nuclei detection methods.},
  articleno    = {e54068},
  author       = {De Vylder, Jonas and Aelterman, Jan and Lepez, Trees and Vandewoestyne, Mado and Douterloigne, Koen and Deforce, Dieter and Philips, Wilfried},
  issn         = {1932-6203},
  journal      = {PLOS ONE},
  keyword      = {SOFTWARE,EXTRACTION,SHAPE PRIOR,IMAGE SEGMENTATION,ACTIVE CONTOUR MODEL,MICROSCOPY,TRACKING,CIRCLES,GAS},
  language     = {eng},
  number       = {1},
  pages        = {9},
  title        = {A novel dictionary based computer vision method for the detection of cell nuclei},
  url          = {http://dx.doi.org/10.1371/journal.pone.0054068},
  volume       = {8},
  year         = {2013},
}

Altmetric
View in Altmetric
Web of Science
Times cited: