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.
Structural-Feature-Attribute-Based Segmentation of Optical Images of Bone Slices Using Optimized Pulse Coupled Neural Networks (PCNN)
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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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