Noisy Image Segmentation via Swarm-based Possibilistic C-means
2018, 23(12), pp.35-41
DOI :https://doi.org/10.9708/jksci.2018.23.12.035
Publisher : The Korean Society Of Computer And Information
Research Area : Computer Science
-
In this paper, we propose a swarm-based possibilistic c-means(PCM) algorithm in order to overcome the problems of PCM, which are sensitiveness of clustering performance due to initial cluster center’s values and producing coincident or close clusters. To settle the former problem of PCM, we adopt a swam-based global optimization method which can be provided the optimal initial cluster centers. Furthermore, to settle the latter problem of PCM, we design an adaptive thresholding model based on the optimized cluster centers that yields preliminary clustered and un-clustered dataset. The preliminary clustered dataset plays a role of preventing coincident or close clusters and the un-clustered dataset is lastly clustered by PCM. From the experiment, the proposed method obtains a better performance than other PCM algorithms on a simulated magnetic resonance(MR) brain image dataset which is corrupted by various noises and bias-fields.
-
[journal] R. Krishnapuram / 1993 / A possibilistic approach to clustering / IEEE Transactions on Fuzzy Systems 1 (2) : 98~110
[journal] J. C. Dunn / 1973 / A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters / Journal of Cybernetics 3 (3) : 32~57
[journal] M. Barni / 1996 / Comments on a possibilistic approach to clustering / IEEE Transactions on Fuzzy Systems 4 (3) : 393~396
[journal] N. R. Pal / 2005 / A possibilistic fuzzy c-means clustering algorithm / IEEE Transactions on Fuzzy Systems 13 (4) : 517~530
[confproc] L. Berger / 2017 / An Adaptive Sampling Scheme to Efficiently Train Fully Convolutional Networks for Semantic Segmentation / Proceedings of Medical Image Understanding and Analysis : 277~286
[journal] A. P. Reeves / 2017 / Large-scale image region documentation for fully automated image biomarker algorithm development and evaluation / Journal of Medical Imaging 4 (2) : 101~115
[journal] M. Dorigo / 1999 / Ant algorithms for discrete optimization / Artificial Life 5 : 137~172
[confproc] J. Kennedy / 1995 / Particle Swarm Optimization / Proceedings of IEEE International Conference on Neural Networks : 1942~1948
[journal] P. Shunmugapriya / 2017 / A hybrid algorithm using ant and bee colony optimization for feature selection and classification (AC-ABC Hybrid) / Swarm and Evolutionary Computation 36 : 27~36
[journal] Y. Zhang / 2011 / Image segmentation using PSO and PCM with Mahalanobis distance / Expert Systems with Applications 38 (7) : 9036~9040
[journal] R. Cheng / 2015 / A Competitive Swarm Optimizer for Large Scale Optimization / IEEE Transactions on Cybernetics 45 (2) : 191~204
[journal] Z. Yong / 2016 / Feature selection of unreliable data using an improved multi-objective PSO algorithm / Neurocomputing 171 (1) : 1281~1290
[web] / McConnel Brain Imaging Gentre (BIC) / http://www.bic.mni.mcgill.ca/brainweb
[journal] S. S. M. Salehi / 2017 / Auto-Context Convolutional Neural Network (Auto-Net) for Brain Extraction in Magnetic Resonance Imaging / IEEE Transactions on Medical Imaging 36 : 2319~2330
[journal] S. Zhichaoa / 2017 / A robust adaptive particle swarm optimization for clustering analysis based on steepest descent method / Journal of Intelligent & Fuzzy Systems 32 (1) : 23~33
-
KCI Citation (0)
107
Viewed
Print this page
Download PDF