In this work, we apply an image segmentation technique that uses pulse coupled neural networks to automatically discern the micro-features of cortical bone histology. In order to properly identify them, we exploit the geometric attributes of these micro features namely shape (i.e., circular or elliptical). These micro-constituent attributes are used as targets for the fitness function of the optimization method (particle swarm optimization, PSO) that was combined with PCNN along with an adaptive threshold, (T) that finds the best value for T between two segmented regions. The result is an optimal set of PCNN parameters that was found in this work to yield good-quality segmented pulses of the various micro-features of 2 different cortical bone images.
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ASME 2013 International Mechanical Engineering Congress and Exposition
November 15–21, 2013
San Diego, California, USA
Conference Sponsors:
- ASME
ISBN:
978-0-7918-5622-2
PROCEEDINGS PAPER
Structural-Feature-Attribute-Based Segmentation of Optical Images of Bone Slices Using Optimized Pulse Coupled Neural Networks (PCNN)
Ilige S. Hage,
Ilige S. Hage
American University of Beirut, Beirut, Lebanon
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Ramsey F. Hamade
Ramsey F. Hamade
American University of Beirut, Beirut, Lebanon
Search for other works by this author on:
Ilige S. Hage
American University of Beirut, Beirut, Lebanon
Ramsey F. Hamade
American University of Beirut, Beirut, Lebanon
Paper No:
IMECE2013-62265, V03BT03A019; 5 pages
Published Online:
April 2, 2014
Citation
Hage, IS, & Hamade, RF. "Structural-Feature-Attribute-Based Segmentation of Optical Images of Bone Slices Using Optimized Pulse Coupled Neural Networks (PCNN)." Proceedings of the ASME 2013 International Mechanical Engineering Congress and Exposition. Volume 3B: Biomedical and Biotechnology Engineering. San Diego, California, USA. November 15–21, 2013. V03BT03A019. ASME. https://doi.org/10.1115/IMECE2013-62265
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