Performance Analysis of Brain Tumor Detection Based On Image Fusion

Medical Image fusion plays a vital role in medical
field to diagnose the brain tumors which can be classified as benign
or malignant. It is the process of integrating multiple images of the
same scene into a single fused image to reduce uncertainty and
minimizing redundancy while extracting all the useful information
from the source images. Fuzzy logic is used to fuse two brain MRI
images with different vision. The fused image will be more
informative than the source images. The texture and wavelet features
are extracted from the fused image. The multilevel Adaptive Neuro
Fuzzy Classifier classifies the brain tumors based on trained and
tested features. The proposed method achieved 80.48% sensitivity,
99.9% specificity and 99.69% accuracy. Experimental results
obtained from fusion process prove that the use of the proposed
image fusion approach shows better performance while compared
with conventional fusion methodologies.

• S. Anbumozhi
• P. S. Manoharan

• Image fusion
• Fuzzy rules
• Neuro-fuzzy classifier.

[1] T. Zaveri, and M. Zaveri, "A Novel Region Based Multimodality Image
Fusion Method”, Journal of Pattern Recognition Research, vol. 2, pp.
140–153, 2011.
[2] V. Tsagaris, V. Anastassopoulos, and G. Lampropoulos, "Fusion of
hyperspectral data using segmented PCT for enhanced color
representation”, IEEE Trans. Geosci. Remote Sens., vol. 43, no. 10, pp.
2365–2375, 2005.
[3] G. Bhatnagar, Q. M. J. Wu, and Z. Liu, "Directive Contrast Based
Multimodal Medical Image Fusion in NSCT Domain. IEEE
Transactions on Multimedia, vol. 15, no. 5, pp. 1014–1024, 2013. DOI:
10.1109/TMM.2013.2244870.
[4] A. Rana, and S. Arora, "Comparative Analysis of Medical Image
Fusion. International Journal of Computer Applications,” vol. 73, no. 9,
pp.10–13, 2013. DOI: 10.5120/12768-9371.
[5] Pinki Jain, and Anu Aggarwal, "Text Fusion in Medical Images using
Fuzzy Logic based Matrix Scanning Algorithm,” International Journal
of Scientific and Research Publications, vol. 2, no. 6, pp. 1-6, 2012.
[6] R. J. Sapkal, and S. M. Kulkarni, "Image Fusion based on Wavelet
Transform for Medical Application,” International Journal of
Engineering Research and Applications, vol. 2, no. 5, pp. 624-627,
2012.
[7] H. Rabbani, R. Nezafat, and S. Gazor, "Wavelet-domain medical image
denoising using bivariate Laplacian mixture model,” IEEE Transactions
on Biomedical Engineering, vol. 56,no. 12,pp. 2826–2837, 2009. DOI:
10.1109/TBME.2009.2028876.
[8] S. Li, H. Yin, and L. Fang, "Group-Sparse representation with
Dictionary Learning for Medical Image Denoising and Fusion. IEEE
Transactions on Biomedical Engineering,” vol. 59, no. 12, pp. 3450-
3459, 2012. DOI: 10.1109/TBME.2012.2217493.
[9] Neha Tirpude, and Rashmi Welekar, "Automated Detection and
Extraction of Brain Tumor from MRI Images”, International Journal of
Computer Applications (0975 – 8887), vol. 77, no. 4, September 2013.
[10] C. Gonzalez, S. Sanchez, Abel Paz, J. Resano, D. Mozos, and A. Plaza,
"Use of FPGA or GPU-based architectures for remotely sensed
hyperspectral image processing,” INTEGRATION, The VLSI Journal,
vol. 46, no. 2, pp. 89–103, March 2013.
[11] N. Jacobson, M. Gupta, and J. Cole, "Linear fusion of image sets for
display,” IEEE Trans. Geosci. Remote Sens., vol. 45, pp. 3277– 3288,
2007.
[12] R. M. Haralick, K. Shanmugan, and I. Dinstein, "Textural Features for
Image Classification”, IEEE Transactions on Systems, Man, and
Cybernetics, vol. SMC-3, pp. 610-621, 1973.