Abstract

Medical images are widely used by the physicians to find abnormalities in human bodies. However the images sometimes are corrupted with a noise which normally exist or occurs during storage, or while transfer the image and sometimes while handling the devices. Therefore the need to enhance the image is crucial in order to improve the image quality. Segmentation technique for Magnetic Resonance Imaging (MRI) of the brain is one of the method used by radiographer to detect any abnormality happened specifically for brain. In this study we have proposed a method for segment the normal and abnormal tissues in the MRI images. At first we select the input image from the BMRI database. Then apply the skull stripping method to the input brain image. After that the proposed method perform the segmentation technique with the help of improved artificial neural networks here the weights are optimized by means of adaptive genetic algorithm. After the classification, the normal tissues like White Matter (WM), Grey Matter (GM) and Cerebrospinal Fluid (CSF) are segmented from the normal BMRI image. Abnormal tissues like Tumor and Edema are segmented from the abnormal BMRI images. The abnormal tissue segmentation will be carried out by optimal SVD and HHT based segmentation in which optimization can be done by Binary cuckoo search algorithm. Both the classification and the segmentation performance of the proposed technique are evaluated in terms of accuracy, sensitivity and specificity. The implementation of the proposed method is done in the working platform of MATLAB.

Keywords:

Adaptive genetic algorithm, artificial neural network, binary cuckoo search, Hilbert Huang transform, singular value decomposition,

References

1. Ahmed, S., K.M. Iftekharuddin and A. Vossough, 2011. Efficacy of texture, shape, and intensity feature fusion for posterior-fossa tumor segmentation in MRI. IEEE T. Inf. Technol. B., 15(2).
   
2. Anwar, A. and A. Iqbal, 2013. Image processing technique for brain abnormality detection. Int. J. Image Process., 7(1).
   
3. Bianchi, A., B. Bhanu, V. Donovan and A. Obenaus, 2014. Visual and contextual modeling for the detection of repeated mild traumatic brain injury. IEEE T. Med. Imaging, 33(1): 11-22.
   CrossRef    PMid:23797243    
4. Damianou, C., K. Ioannides, V. Hadjisavvas, N. Mylonas, A. Couppis and D. Iosif, 2009. In vitro and In vivo brain ablation created by high-intensity focused ultrasound and monitored by MRI. IEEE T. Ultrason. Ferr., 56(6).
   
5. Iscan, Z., Z. Dokur and T. Ölmez, 2010. Tumor detection by using Zernike moments on segmented magnetic resonance brain images. Expert Syst. Appl., 37(3): 2540-2549.
   CrossRef    
6. Islam, A., S.M.S. Reza and K.M. Iftekharuddin, 2013. Multifractal texture estimation for detection and segmentation of brain tumors. IEEE T. Bio-Med. Eng., 60(11).
   
7. Jabbar, N.I. and M. Mehrotra, 2008. Application of fuzzy neural network for image tumor description. Proc. Wrld. Acad. Sci. E., Vol. 34.
   
8. Jaya, J., K. Thanushkodi and M. Karnan, 2009. Tracking algorithm for de-noising of MR brain images. Int. J. Comput. Sci. Netw. Secur., 9(11): 262-267.
   
9. Kwon, D., M. Niethammer, H. Akbari, M. Bilello, C. Davatzikos and K.M. Pohl, 2014. PORTR: Pre-operative and post-recurrence brain tumor registration. IEEE T. Med. Imaging, 33(3): 651-667.
   CrossRef    PMid:24595340 PMCid:PMC4134002    
10. Liu, J., M. Li, J. Wang, F. Wu, T. Liu and Y. Pan, 2014. A survey of MRI-based brain tumor segmentation methods. Tsinghua Sci. Technol., 19(6): 578-595.
    CrossRef    
11. Schwarz, D., T. Kasparek, I. Provaznik and J. Jarkovsky, 2007. A deformable registration method for automated morphometry of MRI brain images in neuropsychiatric research. IEEE T. Med. Imaging, 26(4): 452-461.
    CrossRef    PMid:17427732    
12. Sharma, N. and L.M. Aggarwal, 2010. Automated medical image segmentation techniques. J. Med. Phys., 35: 3-14.
    CrossRef    PMid:20177565 PMCid:PMC2825001    
13. Shen, S., A.J. Szameitat and A. Sterr, 2008. Detection of infarct lesions from single MRI modality using inconsistency between voxel intensity and spatial location--a 3-D automatic approach. IEEE T. Inf. Technol. B., 12(4): 532-540.
    CrossRef    PMid:18632333    
14. Sindhumol, S., Anilkumar and K. Balakrishnan, 2013. Abnormality detection from multispectral brain MRI using multiresolution independent component analysis. Int. J. Signal Process. Image Process. Pattern Recogn., 6(1).
    
15. Zacharaki, E.I. and A. Bezerianos, 2012. Abnormality segmentation in brain images via distributed estimation. IEEE T. Inf. Technol. B., 16(3): 330-338.
    CrossRef    PMid:22157062    
16. Zuo, W., K. Wang, D. Zhang and H. Zhang, 2004. Combination of polar edge detection and active contour model for automated tongue segmentation. Proceeding of the 3rd International Conference on Image and Graphics, pp: 270-273.
    

Competing interests

The authors have no competing interests.

Open Access Policy

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Copyright

The authors have no competing interests.