Multimodality Medical Image Fusion Using M-Band Wavelet and Daubechies Complex Wavelet Transform for Radiation Therapy

Multimodality Medical Image Fusion Using M-Band Wavelet and Daubechies Complex Wavelet Transform for Radiation Therapy

Satishkumar S. Chavan (Don Bosco Institute of Technology, India) and Sanjay N. Talbar (S.G.G.S Institute of Engineering and Technology, India)
Copyright: © 2017 |Pages: 23
DOI: 10.4018/978-1-5225-0549-5.ch018


The process of enriching the important details from various modality medical images by combining them into single image is called multimodality medical image fusion. It aids physicians in terms of better visualization, more accurate diagnosis and appropriate treatment plan for the cancer patient. The combined fused image is the result of merging of anatomical and physiological variations. It allows accurate localization of cancer tissues and more helpful for estimation of target volume for radiation. The details from both modalities (CT and MRI) are extracted in frequency domain by applying various transforms and combined them using variety of fusion rules to achieve the best quality of images. The performance and effectiveness of each transform on fusion results is evaluated subjectively as well as objectively. The fused images by algorithms in which feature extraction is achieved by M-Band Wavelet Transform and Daubechies Complex Wavelet Transform are superior over other frequency domain algorithms as per subjective and objective analysis.
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Medical imaging and its applications are playing a vital role in modern health care practices. Medical imaging has variety of acquisition techniques viz. X-rays, Ultrasound Guided imaging (USG), Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Magnetic Resonance Angiography (MRA), Positron Emission Tomography (PET), Single-Photon Emission Computed Tomography (SPECT) etc. (Depeursinge, Rodriguez, Ville, & Muller, 2014). These imaging technologies are very effectively used by physicians in disease diagnosis, treatment and post treatment analysis. Medical imaging involves acquisition of images from internal organs of the body non-invasively. These medical imaging techniques are applicable to almost every organ of human body. Araki et al. (2014) has used medical imaging for cardio-logical risk analysis using quantification of coronary calcium, whereas liver disease classification is achieved using ultrasound imaging by Suri et al. (2015). Another application of medical imaging is estimation of coronary calcium volumes using Intravascular Ultrasound (IVUS) and generating a relationship with automated carotid intima-media thickness (cIMT) for atherosclerosis disease (Araki et al., 2014; Araki et al. 2015). Intravascular ultrasound (IVUS) image registration using Rigid, Affine, B-Splines and Demons techniques are presented by Araki et al. (2015). The Intravascular Ultrasound (IVUS) bulb images with varying resolutions and imaging conditions for carotid bulb localization and bulb edge detection were experimented and stronger correlation between carotid intima-media thickness (cIMT) and coronary Synergy between percutaneous coronary intervention with TAXUS and cardiac surgery (SYNTAX) score is estimated (Ikeda et al., 2014; Ikeda et al., 2015).

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