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Fusion Algorithm For Medical Images Using Non-Subsampled Contourlet Transform
[Full Text]
AUTHOR(S)
Bhuvaneswari Balachander, D.Dhanasekaran
KEYWORDS
Multi-modality, MRI, CT, Weighted averaging method, Non-subsampled contourlet Transform, image fusion, radiation therapy, treatment planning, medical images.
ABSTRACT
Medical Image fusion has become an integral part in diagnosis and treatment planning. Medical Image fusion is the process of combining two or more images from different modalities to form a new image containing complemented details than the source images. Image fusion improves resolution of the image thereby helping the medical practitioners in determining the position of the disease. Fusion of medical images for different cross-sectional modalities is widely used, mostly where functional images are fused with anatomical data. Ultrasound has for some time now been the standard imaging technique used for treatment planning in prostate cancer, Alzheimer’s, stroke, Glioma, dementia and many more. While this approach is laudable and has yielded some positive results, latest developments have been the integration of images from ultrasound and other modalities such as PET-CT to complement the missing properties of ultrasound images. Non-subsampled contourlet transform based image fusion is proposed that will help greatly in modern radiation therapy treatment planning.
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