CAJ | 학술논문

针对传统C-V图像分割模型在分割效率和准确性两方面的不足，本文提出了一种改进的C-V图像分割模型：一，在模型中加入内部能量项，使水平集函数被限定为符号距离函数，从而避免了水平集函数的重新初始化，提高了图像分割的效率。二，选取Heaviside函数的新正则化函数，使其逼近效果更佳，提高了图像分割的准确性。三，用正实数函数去替换传统C-V模型中Dirac函数的正则化函数，一方面，消除了后者对非初始活动轮廓线附近同质区域边界检测的抑制作用，进而使模型具有更好的全局优化特性，提高了图像分割的准确性；另一方面，使得模型的计算更为简单，提高了图像分割的效率。数值实验表明本文提出的改进C-V图像分割模型提高了图像分割的效率与准确性。
침대전통C-V도상분할모형재분할효솔화준학성량방면적불족，본문제출료일충개진적C-V도상분할모형：일，재모형중가입내부능량항，사수평집함수피한정위부호거리함수，종이피면료수평집함수적중신초시화，제고료도상분할적효솔。이，선취Heaviside함수적신정칙화함수，사기핍근효과경가，제고료도상분할적준학성。삼，용정실수함수거체환전통C-V모형중Dirac함수적정칙화함수，일방면，소제료후자대비초시활동륜곽선부근동질구역변계검측적억제작용，진이사모형구유경호적전국우화특성，제고료도상분할적준학성；령일방면，사득모형적계산경위간단，제고료도상분할적효솔。수치실험표명본문제출적개진C-V도상분할모형제고료도상분할적효솔여준학성。
Aiming at the deficiency of the traditional C-V model for image segmentation in terms of efficiency and accuracy of segmentation, this paper presents an improved C-V image segmentation model. Firstly, the level set function is restricted as a signed distance function by adding the internal energy term in the model, which could avoid the re-initialization and improve the efficiency of image segmentation. Secondly, the new regularization function of Heaviside function is chosen to improve the approximation effect and the accuracy of image segmentation. Finally, the regularization function is applied to replace the traditional Dirac function in C-V model with positive real functions. On the one hand, it’s able to eliminate the latter inhibition of homogeneous areas near the border to detect non-initial active contour lines, and then makes the better global optimization features to improve the accuracy of image segmentation; on the other hand, it gives more simple model and improves the efficiency of image segmentation. Compared with the original C-V model, the numerical experiments show that the improved model has better efficiency and higher accuracy.