MR Brain Image Segmentation using Bacteria Foraging Optimization Algorithm

-The most important task in digital image processing is image segmentation. This paper put forward an unique image segmentation algorithm that make use of a Markov Random Field (MRF) hybrid with biologically inspired technique Bacteria Foraging Optimization Algorithm (BFOA) for Brain Magnetic Resonance Images The proposed new algorithm works on the image pixel data and a region/neighborhood map to form a context in which they can merge. Hence, the MR brain image is segmented using MRF-BFOA and the results are compared to traditional metaheuristic segmentation method Genetic Algorithm. All the experiment results show that MRF-BFOA has better performance than that of standard MRF-GA Keyword Magnetic Resonance Image ( MRI), Brain Tumor, Brain Image Segmentation, Markov Random Field, Bacteria Foraging Optimization Algorithm (BFOA)

[1]  Marco Loog,et al.  Integrating automatic and interactive brain tumor segmentation , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[2]  Jayaram K. Udupa,et al.  Fuzzy connectedness and image segmentation , 2003, Proc. IEEE.

[3]  F. K. Lam,et al.  A fast deformable region model for brain tumor boundary extraction , 2002, Proceedings of the Second Joint 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society] [Engineering in Medicine and Biology.

[4]  Ross T. Whitaker,et al.  Interactive, GPU-Based Level Sets for 3D Brain Tumor Segmentation , 2003 .

[5]  Hongwei Mo,et al.  Image Segmentation Based on Bacterial Foraging and FCM Algorithm , 2011, Int. J. Swarm Intell. Res..

[6]  Xie Yuan-dan,et al.  Survey on Image Segmentation , 2002 .

[7]  S. Murugavalli,et al.  An Improved Implementation of Brain Tumor Detection Using Segmentation Based on Neuro Fuzzy Technique , 2007 .

[8]  Barrie M. Baker,et al.  A genetic algorithm for the vehicle routing problem , 2003, Comput. Oper. Res..

[9]  Moncef Gabbouj,et al.  Center weighted median filters: Some properties and their applications in image processing , 1994, Signal Process..

[10]  Annette Sterr,et al.  MRI fuzzy segmentation of brain tissue using neighborhood attraction with neural-network optimization , 2005, IEEE Transactions on Information Technology in Biomedicine.

[11]  Kevin M. Passino,et al.  Biomimicry of bacterial foraging for distributed optimization and control , 2002 .

[12]  Duane DeSieno,et al.  Adding a conscience to competitive learning , 1988, IEEE 1988 International Conference on Neural Networks.

[13]  Leandro Nunes de Castro,et al.  Recent Developments In Biologically Inspired Computing , 2004 .

[14]  Christos Davatzikos,et al.  PROBABILISTIC SEGMENTATION OF BRAIN TUMORS BASED ON MULTI-MODALITY MAGNETIC RESONANCE IMAGES , 2007, 2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[15]  Christoph Meinel,et al.  Segmentation and quantification of brain tumor , 2004, 2004 IEEE Symposium on Virtual Environments, Human-Computer Interfaces and Measurement Systems, 2004. (VCIMS)..

[16]  Mohammed Yakoob Siyal,et al.  An intelligent modified fuzzy c-means based algorithm for bias estimation and segmentation of brain MRI , 2005, Pattern Recognit. Lett..

[17]  W E Phillips,et al.  Application of fuzzy c-means segmentation technique for tissue differentiation in MR images of a hemorrhagic glioblastoma multiforme. , 1995, Magnetic Resonance Imaging.

[18]  Moncef Gabbouj,et al.  A Fast Algorithm for Running Computation of Center Weighted Rank Order Filters , 1993, IEEE Winter Workshop on Nonlinear Digital Signal Processing.

[19]  Thomas Stützle,et al.  Ant colony optimization: artificial ants as a computational intelligence technique , 2006 .

[20]  Hao He,et al.  Artificial Life for Image Segmentation , 2001, Int. J. Pattern Recognit. Artif. Intell..

[21]  Hamid Soltanian-Zadeh,et al.  Automated Segmentation of Brain Structure from MRI , 2003 .

[22]  Ghassan Hamarneh,et al.  Simultaneous Segmentation, Kinetic Parameter Estimation, and Uncertainty Visualization of Dynamic PET Images , 2007, MICCAI.

[23]  T. Logeswari,et al.  An improved implementation of brain tumor detection using segmentation based on soft computing , 2010 .

[24]  Yuan Yan Tang,et al.  Adaptive Image Segmentation With Distributed Behavior-Based Agents , 1999, IEEE Trans. Pattern Anal. Mach. Intell..