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Spatial Weighted Fuzzy C-Means Clustering Based Principal Component Averaging Image Fusion

R. Vijayarajan, S. Muttan


In medical imaging, image slices of same region observed by same modality may demonstrate complementary image details. Thus, fusion of these images may result in images with more information content and hence leads to better image analysis. In this paper, a principal component averaging fusion method based on segmented regions of source images is proposed. Segmented regions are obtained by applying a robust spatial weighted fuzzy C-means clustering algorithm on the source images. Spatial weighted algorithms always lead to better segmentation by incorporating spatial information on the pixel of interest, thus lead to less noise sensitive fuzzy C-means clustering. Effectiveness of this algorithm is evaluated by conducting experiments on the source images and the images corrupted by additive white Gaussian noise. Experiments and subsequent evaluation of performance metrics also reveals the superiority of the proposed method over other fusion algorithms.


Additive white Gaussian noise, clustering, image fusion, principal components, spatial weighted FCM

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