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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, Sanjay N. Talbar
Copyright: © 2017 |Pages: 23
ISBN13: 9781522505495|ISBN10: 1522505490|EISBN13: 9781522506386
DOI: 10.4018/978-1-5225-0549-5.ch018
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MLA

Chavan, Satishkumar S., and Sanjay N. Talbar. "Multimodality Medical Image Fusion Using M-Band Wavelet and Daubechies Complex Wavelet Transform for Radiation Therapy." Oncology: Breakthroughs in Research and Practice, edited by Information Resources Management Association, IGI Global, 2017, pp. 519-541. https://doi.org/10.4018/978-1-5225-0549-5.ch018

APA

Chavan, S. S. & Talbar, S. N. (2017). Multimodality Medical Image Fusion Using M-Band Wavelet and Daubechies Complex Wavelet Transform for Radiation Therapy. In I. Management Association (Ed.), Oncology: Breakthroughs in Research and Practice (pp. 519-541). IGI Global. https://doi.org/10.4018/978-1-5225-0549-5.ch018

Chicago

Chavan, Satishkumar S., and Sanjay N. Talbar. "Multimodality Medical Image Fusion Using M-Band Wavelet and Daubechies Complex Wavelet Transform for Radiation Therapy." In Oncology: Breakthroughs in Research and Practice, edited by Information Resources Management Association, 519-541. Hershey, PA: IGI Global, 2017. https://doi.org/10.4018/978-1-5225-0549-5.ch018

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Abstract

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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