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.