Advertisement

InFeST – ImageJ Plugin for Rapid Development of Image Segmentation Pipelines

  • Wojciech Marian Czarnecki
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 242)

Abstract

In this paper we present a ImageJ plugin for easy development of image segmentation (clustering) pipelines. Main focus of our approach is to provide scientists working with various images (especially biological and medical ones) with a tool making development of segmentation pipelines fast and easy. We accomplish this by introducing an extra abstraction layer to the ImageJ image segmentation approach – the feature space projection – that enables us to work with complex image descriptors and manage, visualize and test them directly from the plugin. Furthermore we give three separate ways of expressing such projections – one based on Java language, one based on external scripting and one on our custom simple Micro Matrix Language. The plugin can also serve as a fast method of rapid prototyping of image filters while its full ImageJ macro support makes it really easy to include it in ones current image processing methods.

Keywords

imageJ image processing segmentation feature space projection 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Andlauer, T.F.M., Sigrist, S.J.: Quantitative analysis of Drosophila larval neuromuscular junction morphology. Cold Spring Harbor Protocols 2012(4), 490–493 (2012)Google Scholar
  2. 2.
    Carreras, I.A.: Advanced WEKA Segmentation (2011), http://fiji.sc/wiki/index.php/Advanced_Weka_Segmentation
  3. 3.
    Dello, S.A.W.G., Stoot, J.H.M.B., van Stiphout, R.S.A., Bloemen, J.G., Wigmore, S.J., Dejong, C.H.C., van Dam, R.M.: Prospective volumetric assessment of the liver on a personal computer by nonradiologists prior to partial hepatectomy. World Journal of Surgery 35(2), 386–392 (2011)CrossRefGoogle Scholar
  4. 4.
    Dorcet, V., Larose, X., Fermin, C., Bissey, M., Boullay, P.: Extrax: an ImageJ plug-in for electron diffraction intensity extraction. Journal of Applied Crystallography 43(1), 191–195 (2010)CrossRefGoogle Scholar
  5. 5.
    Federici, F., Dupuy, L., Laplaze, L., Heisler, M., Haseloff, J.: Integrated genetic and computation methods for in planta cytometry. Nature Methods 9(5), 483–485 (2012)CrossRefGoogle Scholar
  6. 6.
    Kim, U.S., Kim, S.J., Baek, S.H., Kim, H.K., Sohn, Y.H.: Quantitative analysis of optic disc color. Korean Journal of Ophthalmology 25(3), 174–177 (2011)CrossRefGoogle Scholar
  7. 7.
    Schmid, B., Helfrich-Förster, C., Yoshii, T.: A new ImageJ plug-in “ActogramJ” for chronobiological analyses. Journal of Biological Rhythms 26(5), 464–467 (2011)CrossRefGoogle Scholar
  8. 8.
    Schneider, C.A., Rasband, W.S., Eliceiri, K.W.: NIH image to ImageJ: 25 years of image analysis. Nature Methods 9(7), 671–675 (2012)CrossRefGoogle Scholar
  9. 9.
    Smith, M.B., Li, H., Shen, T., Huang, X., Yusuf, E., Vavylonis, D.: Segmentation and tracking of cytoskeletal filaments using open active contours. Cytoskeleton 67(11), 693–705 (2010)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Faculty of Mathematics and Computer ScienceAdam Mickiewicz University in PoznanPoznanPoland

Personalised recommendations