STRUCTURAL ANNOTATION OF EM IMAGES BY GRAPH CUT

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Biological images have the potential to reveal complex signatures that may not be amenable to morphological modeling in terms of shape, location, texture, and color. An effective analytical method is to characterize the composition of a specimen based on user-defined patterns of texture and contrast formation. However, such a simple requirement demands an improved model for stability and robustness. Here, an interactive computational model is introduced for learning patterns of interest by example. The learned patterns bound an active contour model in which the traditional gradient descent optimization is replaced by the more efficient optimization of the graph cut methods. ... continued below

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Chang, Hang; Auer, Manfred & Parvin, Bahram May 8, 2009.

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Biological images have the potential to reveal complex signatures that may not be amenable to morphological modeling in terms of shape, location, texture, and color. An effective analytical method is to characterize the composition of a specimen based on user-defined patterns of texture and contrast formation. However, such a simple requirement demands an improved model for stability and robustness. Here, an interactive computational model is introduced for learning patterns of interest by example. The learned patterns bound an active contour model in which the traditional gradient descent optimization is replaced by the more efficient optimization of the graph cut methods. First, the energy function is defined according to the curve evolution. Next, a graph is constructed with weighted edges on the energy function and is optimized with the graph cut algorithm. As a result, the method combines the advantages of the level set method and graph cut algorithm, i.e.,"topological" invariance and computational efficiency. The technique is extended to the multi-phase segmentation problem; the method is validated on synthetic images and then applied to specimens imaged by transmission electron microscopy(TEM).

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  • Sixth IEEE International Symposium on Biomedical Imaging, Boston, MA

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  • Report No.: LBNL-2222E
  • Grant Number: DE-AC02-05CH11231
  • Office of Scientific & Technical Information Report Number: 965888
  • Archival Resource Key: ark:/67531/metadc928045

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Office of Scientific & Technical Information Technical Reports

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  • May 8, 2009

Added to The UNT Digital Library

  • Nov. 13, 2016, 7:26 p.m.

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  • Nov. 18, 2016, 4:18 p.m.

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Chang, Hang; Auer, Manfred & Parvin, Bahram. STRUCTURAL ANNOTATION OF EM IMAGES BY GRAPH CUT, article, May 8, 2009; Berkeley, California. (digital.library.unt.edu/ark:/67531/metadc928045/: accessed November 21, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.