Multiresolution approach on Medical Image Fusion by Modified Local Energy

Abstract

Human and machine perception data that are not redundant are very important in the medical field for diagnosis and treatment. 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 ModifiedLocal Energy Image Fusion(MLEIF). The image edges are essential information points that are used to display a clear visual image structure. 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 observes to identify the lesion part from the fused image. Carried out experiments on real-time and standard data sets with expert radiologist subjective evaluation and quantitative analysis by well-known non-reference performance measures.

Research on denoising and enhancing methods of medical images based on convolutional neural networks

“…As can be seen from Figure 3, the Sigmoid function compresses each input value to a range of 0-1, mapping the input value to 0 when it is very small, and to 1 when it is very large. 15 The sigmoid function has been widely popular, because it has the shape of the exponential function, in terms…”

“…As can be seen from Figure 3, the Sigmoid function compresses each input value to a range of 0–1, mapping the input value to 0 when it is very small, and to 1 when it is very large 15 . The sigmoid function has been widely popular, because it has the shape of the exponential function, in terms of biological sense, it is very similar to neurons, so it is widely used, it can express the meaning of “active” very well, saturated active value is 1, the inactive value is 0.…”