Wavelet Based Image Fusion for Detection of Brain Tumor

Brain tumor, is one of the major causes for the increase in mortality among children and adults. Detecting the regions of brain is the major challenge in tumor detection. In the field of medical image processing, multi sensor images are widely being used as potential sources to detect brain tumor. In this paper, a wavelet based image fusion algorithm is applied on the Magnetic Resonance (MR) images and Computed Tomography (CT) images which are used as primary sources to extract the redundant and complementary information in order to enhance the tumor detection in the resultant fused image. The main features taken into account for detection of brain tumor are location of tumor and size of the tumor, which is further optimized through fusion of images using various wavelet transforms parameters. We discuss and enforce the principle of evaluating and comparing the performance of the algorithm applied to the images with respect to various wavelets type used for the wavelet analysis. The performance efficiency of the algorithm is evaluated on the basis of PSNR values. The obtained results are compared on the basis of PSNR with gradient vector field and big bang optimization. The algorithms are analyzed in terms of performance with respect to accuracy in estimation of tumor region and computational efficiency of the algorithms.

[1]  R P Velthuizen,et al.  MRI segmentation: methods and applications. , 1995, Magnetic resonance imaging.

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

[3]  KaiXing Wu,et al.  Image fusion at pixel level algorithm is introduced and the evaluation criteria , 2010, 2010 International Conference on Educational and Network Technology.

[4]  Brian A. Baertlein,et al.  Feature-Level and Decision-Level Fusion of Noncoincidently Sampled Sensors for Land Mine Detection , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Guy Marchal,et al.  Multimodality image registration by maximization of mutual information , 1997, IEEE Transactions on Medical Imaging.

[6]  G. Marchal,et al.  Multi-modal volume registration by maximization of mutual information , 1997 .

[7]  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)..

[8]  Yufeng Zheng,et al.  An advanced image fusion algorithm based on wavelet transform: incorporation with PCA and morphological processing , 2004, IS&T/SPIE Electronic Imaging.

[9]  B. S. Manjunath,et al.  Multisensor Image Fusion Using the Wavelet Transform , 1995, CVGIP Graph. Model. Image Process..

[10]  B A Porter,et al.  Real-time spatial compound imaging: application to breast, vascular, and musculoskeletal ultrasound. , 2001, Seminars in ultrasound, CT, and MR.

[11]  Suchendra M. Bhandarkar,et al.  Multiscale image segmentation using a hierarchical self-organizing map , 1997, Neurocomputing.

[12]  Richard Kronland-Martinet,et al.  A real-time algorithm for signal analysis with the help of the wavelet transform , 1989 .

[13]  A. Haar.  Zur Theorie der orthogonalen Funktionensysteme , 1910 .

[14]  U. Tiwary,et al.  Feature level fusion of multimodal medical images in lifting wavelet transform domain , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[15]  Rick S. Blum,et al.  A categorization of multiscale-decomposition-based image fusion schemes with a performance study for a digital camera application , 1999, Proc. IEEE.

[16]  P. J. Burt,et al.  The Pyramid as a Structure for Efficient Computation , 1984 .

[17]  Ewald Moser,et al.  Wavelet-based multifractal analysis of fMRI time series , 2004, NeuroImage.

[18]  Shinjiro Kawato,et al.  Gaze Direction Estimation with a Single Camera Based on Four Reference Points and Three Calibration Images , 2006, ACCV.

[19]  Amarjot Singh,et al.  Malignant Brain Tumor Detection , 2012 .

[20]  W. Eric L. Grimson,et al.  A shape-based approach to the segmentation of medical imagery using level sets , 2003, IEEE Transactions on Medical Imaging.

[21]  Marcin Iwanowski.  Image Morphing Based on Morphological Interpolation Combined with Linear Filtering , 2002, WSCG.