Medical Image Fusion Using Wavelet and Curvelet Transform Domains
T Parvathi
PG Scholar, Applied Electronics, Sardar Raja College of Engineering, Alangulam, Tirunelveli, Tamilnadu
B Bala Murugan
Head Of The Department of ECE, Sardar Raja College of Engineering, Alangulam, Tirunelveli, Tamilnadu
Receiving Date:
2016-06-26
Acceptance Date:
2016-08-09
Publication Date:
2016-09-07
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Abstract
Medical image fusion has been used to derive useful information from multimodality medical image data. The idea is to improve the image content by fusing images like computer tomography (CT) and magnetic resonance imaging (MRI) images, magnetic resonance imaging (MRI) provides better information on soft tissue whereas computed tomography (CT) provides better information about denser tissue. In this project a two stage multimodal fusion framework using cascaded combination of Stationary Wavelet Transform (SWT) and Non Sub-sampled Contourlet Transform (NSCT) domains is presented for images acquired using two distinct medical imaging sensor modalities (i.e. MRI and CT-Scan). The major advantage of using a cascaded combination of SWT and NSCT is to improve upon the shift variance, directionality and phase information in the finally fused image. The first stage employs Principal Component Analysis (PCA) algorithm in SWT domain to minimize the redundancy. Maximum fusion rule is then applied in NSCT domain at second stage to enhance contrast of the diagnostic features.
Keywords:
Medical Image Fusion; computer tomography (CT); magnetic resonance imaging (MRI)
References
- S. Casciaro, et al., “An Innovative Ultrasound Signal Processing Technique to selectively Detect Nanosized Contrast Agents in Echographic Images,” IEEE Tran. on Inst. & Mea., 2015.
- A. Alkan, S. A. Tuncer and M. Gunay, 'Comparative MR image analysis for thyroid nodule detection and quantification,' Measurement, vol. 47, pp. 861-868, 2014
- B. S. Hoyle, 'A schema for generic process tomography sensors,' IEEE Sensors Journal, vol. 5, no. 2, pp. 117-124, 2005
- N. I. Giannoccaro, A. Massaro, L. Spedicato and A. Lay-Ekuakille, “Characterization of an Innovative Like-Eye Sensor for Feature Detection and Robot Sensing Measurement,” Measurement, vol. 55, pp.153-167, 2014
- G. Soloperto, et al., “Multiparametric Evaluation of the Acoustic Behaviour of Halloysite Nanotubes for Medical Echographic Image Enhancement,” IEEE Tran. on Inst. & Measurement, vol. 63, no. 6, pp. 1423–1430, 2014
- V. Bhateja, M. Misra, S. Urooj and A. Lay-Ekuakille, “A Robust Polynomial Filtering Framework for Mammographic Image Enhancement from Biomedical Sensors,” IEEE Sensors Journal, vol. 13, no. 11, pp. 4147-4156, November 2013
- V. Bhateja, S. Urooj and M. Misra, “Technical Advancements to Mobile Mammography using Non-Linear Polynomial Filters and IEEE 21451-1 Information Model,” IEEE Sensors Journal, vol. 15, no. 5, pp. 1-10, pp. 2559-2566, May 2015.
- B. V. Dasarathy, “Information Fusion in the Realm of Medical Applications – A Bibliographic Glimpse at its Growing Appeal, Information Fusion,” vol. 13, no. 1, pp. 1-9, January 2012
- Himanshi, V. Bhateja, A. Krishn and A. Sahu, “An Improved Medical Image Fusion Approach Using PCA and Complex Wavelets,” Proc. Int. Conf. on Med. Imag., m-Health & Emerg. Comm. Syst. (MEDCom-2014), pp. 442-447, November 2014
- V. Bhateja, H. Patel, A. Krishn and A. Sahu, “Medical Image Fusion in Wavelet and Ridgelet Domains: A Comparative Evaluation,” IJRSDA, vol. 2, no. 2, pp. 78-91, July 2015.
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