Vishwambhar Pathak; Praveen Dhyani; Prabhat Mahanti

Autonomous Image Segmentation using Density-Adaptive Dendritic Cell Algorithm

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Author(s)

Vishwambhar Pathak ^1,* Praveen Dhyani ² Prabhat Mahanti ³

1. Department of CSE, Birla Institute of Technology (Ranchi) Jaipur Campus, 27, MIA, Jaipur

2. Banasthali University, Jaipur Campus, C-62, Sarojini Marg, C-Scheme,Jaipur

3. University of New Brunswick, St. John, Canada

* Corresponding author.

DOI: https://doi.org/10.5815/ijigsp.2013.10.04

Received: 26 Apr. 2013 / Revised: 1 Jun. 2013 / Accepted: 29 Jun. 2013 / Published: 8 Aug. 2013

Index Terms

Grey scale image segmentation, autonomous segmentation, Gaussian mixture model, non-parametric estimation, artificial immune system (AIS), Dendritic Cell Algorithm (DCA)

Abstract

Contemporary image processing based applications like medical diagnosis automation and analysis of satellite imagery include autonomous image segmentation as inevitable facility. The research done shows the efficiency of an adaptive evolutionary algorithm based on immune system dynamics for the task of autonomous image segmentation. The recognition dynamics of immune-kernels modeled with infinite Gaussian mixture models exhibit the capability to automatically determine appropriate number of segments in presence of noise. In addition, the model using representative density-kernel-parameters processes the information with much reduced space requirements. Experiments conducted with synthetic images as well as real images recorded assured convergence and optimal autonomous model estimation. The segmentation results tested in terms of PBM-index values have been found comparable to those of the Fuzzy C-Means (FCM) for the same number of segments as generated by our algorithm.

Cite This Paper

Vishwambhar Pathak, Praveen Dhyani, Prabhat Mahanti,"Autonomous Image Segmentation using Density-Adaptive Dendritic Cell Algorithm", IJIGSP, vol.5, no.10, pp.26-35, 2013. DOI: 10.5815/ijigsp.2013.10.04

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