Wavelet Based Image Fusion for Medical Applications
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Image fusion is the process by which complementary information from multiple images are integrated such that the resultant image contains more information than the individual source images. Medical image fusion is used to derive useful information from multimodality medical images which provides more information to the doctor for easy diagnosis of diseases. This paper presents the wavelet based image fusion algorithm on different multimodality medical images. Medical images to be fused are decomposed using Stationary Wavelet Transform (SWT). The low frequency coefficients are performed with a maximum-selection (MS) rule and high frequency coefficients are convolved with canny operator followed by the MS rule. The reconstructed image is obtained by performing the inverse SWT. The evaluation parameters Mean, Standard deviation, Average Gradient, Spatial frequency, Mutual Information, Edge based similarity measures are considered for evaluating the fused images. The performance of our method is compared with pixel averaging, PCA, DWT fusion methods. The experimental results show that the proposed framework provides better result for analysis of multimodality medical images.
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