Abstract：Objective To propose a weighted level set evolution based on local edge features for medical image segmentation.Methods Firstly, the local edge features from the adjacent region located inside and outside of the evolving contour were calculated.
Then, the average edge intensity and the image gradient vector flow field of the adjacent contour of the evolving contour were calculated, and the weighted function term was constructed. Finally, the length term and the area term of the energy function of the
new level set algorithm were constructed, and the minimum value was obtained by using the partial differential equation to obtain the ideal boundary of the image target. Synthetic images and clinical images were selected as experimental images. Dice similarity
coefficient (DSC) was used for segmentation performance of different level set algorithms. Results Results of visual analysis showed that the image target contour obtained based on the algorithm had the highest degree of coincidence with the real image target area boundary. Results of quantitative analysis showed that our proposed method could obtain higher DSC than other methods based on this algorithm. Additionally, when the number of iterations was relatively small, the optimal target contour could be obtained by the algorithm in this study, and when the number of iterations was increased, the DSC changed slightly and did not overflow. Finally, with different initial contour positions, the DSC obtained by the other three algorithms varied greatly and were lower than those obtained by this algorithm. Conclusion Compared with other level set algorithms, this algorithm has faster convergence speed, lower sensitivity to the initial contour position and strong stability, so it is a feasible medical image segmentation algorithm.
魏应敏，王薇，张媛. 基于加权水平集演变算法的医学图像分割研究[J]. 中国医疗设备, 2021, 36(1): 94-98.
WEI Yingmin, WANG Wei, ZHANG Yuan. Research on Medical Image Segmentation Based on Weighted Level Set Evolution. China Medical Devices, 2021, 36(1): 94-98.
