Advertisement

Identifying Loose Connective and Muscle Tissues on Histology Images

  • Claudia Mazo
  • Maria Trujillo
  • Liliana Salazar
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8259)

Abstract

Histology images are used to identify biological structures present in living organisms — cells, tissues, and organs — correctly. The structure of tissues varies according to the type and purpose of the tissue. Automatic identification of tissues is an open problem in image processing. In this paper, the identification of loose connective and muscle tissues based on morphological tissue information is presented.

Image identification is commonly evaluated in isolation. This is done either by eye or via some other quality measure. Expert criteria — by eye — are used to evaluate the identification results. Experimental results show that the proposed approach yields results close to the real results, according to expert opinion.

Keywords

Muscle Tissue Automatic Segmentation Structure Tensor Morphological Operation Loose Connective Tissue 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Rao, A.R., Schunck, B.G.: Computing oriented texture fields. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 1989, pp. 61–68 (1989)Google Scholar
  2. 2.
    Weickert, J.: A Scheme for Coherence-Enhancing Diffusion Filtering with Optimized Rotation Invariance. Journal of Visual Communication and Image Representation 13, 103–118 (2002)CrossRefGoogle Scholar
  3. 3.
    Kanungo, T., Mount, D.M., Netanyahu, N.S., Piatko, C.D., Silverman, R., Wu, A.Y.: An efficient k-means clustering algorithm: Analysis and implementation. IEEE Transactions on Pattern Analysis and Machine Intelligence 24, 881–892 (2002)CrossRefGoogle Scholar
  4. 4.
    Nosal, E.-M.: Flood-fill algorithms used for passive acoustic detection and tracking. In: New Trends for Environmental Monitoring Using Passive Systems, pp. 1–5 (2008)Google Scholar
  5. 5.
    Lu, B., Miao, C., Wang, H.: Pixel level image fusion based on linear structure tensor. In: 2010 IEEE Youth Conference on Information Computing and Telecommunications (YC-ICT), pp. 303–306 (2010)Google Scholar
  6. 6.
    Vegue, J.B.: Atlas de Histología y Organografía Microscópica. Editorial Medica Panamericana, S.A. Madrid, España (2011)Google Scholar
  7. 7.
    Mazo, C., Trujillo, M., Salazar, L.: An Automatic Segmentation Approach of Epithelial Cells Nuclei. In: Alvarez, L., Mejail, M., Gomez, L., Jacobo, J. (eds.) CIARP 2012. LNCS, vol. 7441, pp. 567–574. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  8. 8.
    Haralick, R., Shapiro, L.: Computer and Robot Vision, vol. 1, ch. 5. Addison-Wesley Publishing Company (1992)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Claudia Mazo
    • 1
  • Maria Trujillo
    • 1
  • Liliana Salazar
    • 2
  1. 1.School of Computer and Systems EngineeringUniversidad del ValleColombia
  2. 2.Department of MorphologyUniversidad del ValleColombia

Personalised recommendations