Soft Computing in Industrial Applications

Abstract

In this paper, a segmentation system with a modified automatic Seeded Region Growing (SRG) based on Particle Swarm Optimization (PSO) image clustering will be presented. The paper is focused on Magnetic Resonance Imaging (MRI) breast tumour segmentation. The PSO clusters’ intensities are involved in the proposed algorithms of the automated SRG initial seed and threshold value selection. Prior to that, some pre-processing methodologies are involved. And breast skin is detected and deleted using the integration of two algorithms, i.e. Level Set Active Contour and Morphological Thinning. The system is applied and tested on the RIDER breast MRI dataset, and the results are evaluated and presented in comparison to the Ground Truths of the dataset. The results show higher performance compared to the previous segmentation approaches that have been tested on the same dataset. A. Q. Al-Faris (&) U. K. Ngah N. A. M. Isa Imaging and Computational Intelligence Research Group (ICI), Universiti Sains Malaysia, Penang, Malaysia e-mail: alialfaris2009@gmail.com U. K. Ngah e-mail: eeumi@eng.usm.my N. A. M. Isa e-mail: ashidi@eng.usm.my I. L. Shuaib Advanced Medical and Dental Institute (AMDI), Universiti Sains Malaysia, Penang, Malaysia e-mail: ibrahim@amdi.usm.edu.my V. Snasel et al. (eds.), Soft Computing in Industrial Applications, Advances in Intelligent Systems and Computing 223, DOI: 10.1007/978-3-319-00930-8_5, Springer International Publishing Switzerland 2014 49