Padmavathi K, Maya V Karki


Medical image fusion is a technique where multiple imaging modalities are merged together to obtain a single image that contains maximum information. Spatial domain approach such as IHS-PCA fusion provides best visual quality but required large storage space and also lags directional information. A novel technique is proposed here that integrates Dual Tree Complex Wavelet Transform (DTCWT) with PCA using histogram matching and IHS space that improves the fusion of MRI with PET/SPECT images. The multispectral image PET/SPECT with RGB channels are converted to intensity hue and saturation components by IHS transform. Pathological information in the images can be highlighted in multiscale and multidirection by using DTCWT. PCA with weighted average fusion rule is used for extracting tumor features by using principal components. Visual and quantitative analysis show that our algorithm provides high structural information content, high mutual information with high spatial and spectral resolution thereby enhancing the tumor region compared to other methods.


Image fusion, Histogram matching, IHS, DTCWT, PCA, Fusion rules.

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