CAJ | 학술논문

针对距离正则化的水平集演化(the Distance Regularized Level Set Evolution Mode, DRLSE)模型难以处理弱边缘图像、演化效率低问题，提出一种新的基于相位信息的水平集超声图像分割算法(the Distance Regularized Level Set Evolution Mode Based on Phase Congruency, PDRLSE)。该算法利用相位一致性检测原理，构造新的边界指示函数，代替了 DRLSE 模型中的边界停止函数，得到新的能量泛函。实验结果表明：该方法在分割超声图像时，能够较好的分割出甲状腺肿瘤目标，且演化效率也有所提高。
침대거리정칙화적수평집연화(the Distance Regularized Level Set Evolution Mode, DRLSE)모형난이처리약변연도상、연화효솔저문제，제출일충신적기우상위신식적수평집초성도상분할산법(the Distance Regularized Level Set Evolution Mode Based on Phase Congruency, PDRLSE)。해산법이용상위일치성검측원리，구조신적변계지시함수，대체료 DRLSE 모형중적변계정지함수，득도신적능량범함。실험결과표명：해방법재분할초성도상시，능구교호적분할출갑상선종류목표，차연화효솔야유소제고。
As the Distance Regularized Level Set Evolution (DRLSE) model is difficult to deal with weak boundary images and has inefficient curve evolution, a novel ultrasound image segmentation model based on the phase information in a variational level set formulation is proposed. The proposed model constructs a new boundary indicator function by using the theory of phase congruency detection, instead of the boundary stopping function in the model of DRLSE, and then come to the new energy functional. Experimental results show that the method accelerates the evolution speed. Furthermore, it can extract thyroid tumors ideally in the segmentation of ultrasound images.