Multiresolution approach on medical image fusion by modified local energy

Abstract

Human and machine perception data that are not redundant are very important in medical field for diagnosis and treatment. Existing fusion methods lack in complexity and are time-consuming. The proposed work fuses medical images to extract valuable necessary information from dissimilar images to a single image in the wavelet domain using a novel modified local energy (MLE) fusion rule termed MLE image fusion. Modified local energy helps to provide edge characteristics more clearly in the fusion outcome than a single pixel-based fusion rule. The denoising property of the local energy is the additional advantage of the proposed fusion. The similarity of the fused image with the source images is improved by B-Spline registration in the pre-processing stage. Finally, the fused image is created with all the corresponding coefficients by transforming the inverse wavelet. SVM-based contouring of the lesion part helps observe to identify the lesion part from the fused image. The proposed approach assists medical professionals in the diagnosis of lesions or anomalies in tissues. Experiments on real-time and standard datasets with expert radiologist subjective evaluation and quantitative analysis are carried out by well-known non-reference performance measures.