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Self-Adaptive PCNN Based on the ACO Algorithm and its Application on Medical Image Segmentation



Medical image segmentation plays a dominant role in medical image analysis and clinical research. As an effective method for image segmentation, pulse-coupled neural networks (PCNN) has its own limitation that the values of the parameters have a strong effect on its performance when it is used to segment the image. This paper proposed a new method for medical image segmentation using the self-adaptive PCNN model. In this method, we combined the searching capabilities of ant colony optimization (ACO) algorithm in the solution space with the biological characteristics of PCNN, to find the optimal values of PCNN2019s parameters for each input image. Moreover, the search process of the ACO algorithm was divided into the local searching and the global searching to accelerate the speed of the ASO2019s convergence. Based on the above work, a new automatic method for the image segmentation, namely ACO-PCNN, was presented. Lastly, four pairs of different MR medical images, including transaxial, sagittal, coronal sections and noisy medical images, were used to test and validate the performance of the proposed method. The experimental results illustrated that our method was accurate and effective to MRI medical images.



Total Pages: 8
Pages: 303-310


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Volume: 23
Issue: 2
Year: 2016

Cite this document


Yuli Chen et al. "A New Automatic Parameter Setting Method of a Simplified PCNN for Image Segmentation." IEEE Transactions on Neural Networks 22.6 (2011): 880-892. Crossref. Web.

Dorigo, Marco, Mauro Birattari, and Thomas Stutzle. "Ant Colony Optimization." IEEE Computational Intelligence Magazine 1.4 (2006): 28-39. Crossref. Web.

Eckhorn R. Models of brain function: A neural network for feature linking via synchronous activity: Results from cat visual cortex and from simulations

Hage, Ilige S., and Ramsey F. Hamade. "Segmentation of Histology Slides of Cortical Bone Using Pulse Coupled Neural Networks Optimized by Particle-Swarm Optimization." Computerized Medical Imaging and Graphics 37.7-8 (2013): 466-474. Crossref. Web.

Ji, Luping, and Zhang Yi. "A Mixed Noise Image Filtering Method Using Weighted-Linking PCNNs." Neurocomputing 71.13-15 (2008): 2986-3000. Crossref. Web.

Xiangying Jiang. "A Self-Adapting Pulse-Coupled Neural Network Based on Modified Differential Evolution Algorithm and Its Application on Image Segmentation." International Journal of Digital Content Technology and its Applications 6.20 (2012): 501-509. Crossref. Web.

Johnson, J.L., and M.L. Padgett. "PCNN Models and Applications." IEEE Transactions on Neural Networks 10.3 (1999): 480-498. Crossref. Web.

Lan H. Computer Science

Mandal, Devraj, Amitava Chatterjee, and Madhubanti Maitra. "Robust Medical Image Segmentation Using Particle Swarm Optimization Aided Level Set Based Global Fitting Energy Active Contour Approach." Engineering Applications of Artificial Intelligence 35 (2014): 199-214. Crossref. Web.

Martin, D. et al. "A Database of Human Segmented Natural Images and Its Application to Evaluating Segmentation Algorithms and Measuring Ecological Statistics." Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001 n. pag. Crossref. Web.

Mayer, A., and H. Greenspan. "An Adaptive Mean-Shift Framework for MRI Brain Segmentation." IEEE Transactions on Medical Imaging 28.8 (2009): 1238-1250. Crossref. Web.

Otsu, Nobuyuki. "A Threshold Selection Method from Gray-Level Histograms." IEEE Transactions on Systems, Man, and Cybernetics 9.1 (1979): 62-66. Crossref. Web.

Smistad, Erik et al. "Medical Image Segmentation on GPUs - A Comprehensive Review." Medical Image Analysis 20.1 (2015): 1-18. Crossref. Web.

Sreeja, N.K., and A. Sankar. "Ant Colony Optimization Based Binary Search for Efficient Point Pattern Matching in Images." European Journal of Operational Research 246.1 (2015): 154-169. Crossref. Web.

Monica Subashini, M., and Sarat Kumar Sahoo. "Pulse Coupled Neural Networks and Its Applications." Expert Systems with Applications 41.8 (2014): 3965-3974. Crossref. Web.

Tabakhi, Sina, and Parham Moradi. "Relevance-redundancy Feature Selection Based on Ant Colony Optimization." Pattern Recognition 48.9 (2015): 2798-2811. Crossref. Web.

Unnikrishnan, R., and M. Hebert. "Measures of Similarity." 2005 Seventh IEEE Workshops on Applications of Computer Vision (WACV/MOTION”05) - Volume 1 (2005): n. pag. Crossref. Web.

Vincent, L., and P. Soille. "Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations." IEEE Transactions on Pattern Analysis and Machine Intelligence 13.6 (1991): 583-598. Crossref. Web.

Xu, Xinzheng et al. "Particle Swarm Optimization for Automatic Parameters Determination of Pulse Coupled Neural Network." Journal of Computers 6.8 (2011): n. pag. Crossref. Web.

Zhuang, Hualiang, Kay-Soon Low, and Wei-Yun Yau. "Multichannel Pulse-Coupled-Neural-Network-Based Color Image Segmentation for Object Detection." IEEE Transactions on Industrial Electronics 59.8 (2012): 3299-3308. Crossref. Web.


ISSN PRINT: 1079-8587
ISSN ONLINE: 2326-005X
DOI PREFIX: 10.31209
10.1080/10798587 with T&F
IMPACT FACTOR: 0.652 (2017/2018)

SJR: "The two years line is equivalent to journal impact factor ™ (Thomson Reuters) metric."

Journal: 1995-Present


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