Image fusion technology plays an important role in computer-aided diagnosis. Detail extraction and energy preservation are two key issues in image fusion, and the traditional fusion methods address them simultaneously by designing the fusion method. However, it tends to cause information loss or insufficient energy preservation. In view of this, a fusion method was proposed to solve the problems of detail extraction and energy preservation separately. The first part of the method aimed at detail extraction. Firstly, the Non-Subsampled Shearlet Transform (NSST) was used to divide the source image into low-frequency and high-frequency subbands. Then, an improved energy-based fusion rule was used to fuse the low-frequency subbands, and an strategy based on the intuitionistic fuzzy set theory was proposed for the fusion of the high-frequency subbands. Finally, the inverse NSST was employed to reconstruct the image. In the second part, an intensity enhancement method was proposed for energy preservation. The proposed method was verified on 43 groups of images and compared with other eight fusion methods such as Principal Component Analysis (PCA) and Local Laplacian Filtering (LLF). The fusion results on two different categories of medical image fusion (Magnetic Resonance Imaging (MRI) and Positron Emission computed Tomography (PET), MRI and Single-Photon Emission Computed Tomography (SPECT)) show that the proposed method can obtain more competitive performance on both visual quality and objective evaluation indicators including Mutual Information (MI), Spatial Frequency (SF), Q value, Average Gradient (AG), Entropy of Information (EI), and Standard Deviation (SD), and can improve the quality of medical image fusion.

The existing Nakagami parametric imaging of ultrasonic harmonic envelope signals can realize non-invasive monitoring of the ablation process, but it cannot estimate the ablation area accurately. In order to solve the problem, a Gaussian Approximation adaptive Threshold Segmentation (GATS) method based on ultrasonic harmonic envelope Nakagami parameter images was proposed to monitor microwave ablation areas accurately and effectively. Firstly, a high-pass filter was used to obtain the harmonic components of the ultrasound echo Radio Frequency (RF) signal. Then, the Nakagami shape parameters of the harmonic signal envelope were estimated, and Nakagami parameter image was generated by composite window imaging. Finally, Gaussian approximation of Nakagami parameter image was applied to present the ablation area, the anisotropic smoothing preprocessing was performed to the approximated image, and the threshold segmentation of the smoothed image was used to accurately estimate the ablation area. The results of microwave ablation experiments show that, the long and short axis errors of the threshold segmentation ablation area after anisotropic smoothing based on Perona-Malik (P-M) algorithm and the actual ablation area are reduced by 3.15 percentage points and 2.21 percentage points respectively compared with the errors obtained by using Catte algorithm, and decreased by 7.87 percentage points and 5.74 percentage points compared with the errors obtained by using Median algorithm. It can be seen that GATS using P-M algorithm for ultrasonic harmonic envelope Nakagami parameter images can estimate the ablation area more accurately and provide effective monitoring for clinical ablation surgery.