为解决工业计算机层析成像(CT)图像的伪影和弱边缘问题,提出一种基于小波变换的图像区域可伸缩拟合能量最小化分割方法,实现图像边缘的精确定位,从而提高图像测量精度。首先,采用小波变换对图像进行预处理,降低金属伪影。然后,采用所提方法精确分割图像,提高感兴趣区域边缘的定位精度。实际数据测量结果表明,所提方法可有效降低图像弱边缘的影响,测量相对误差低于0.7%,相较Chan-Vese算法,测量精度提高了1.4倍,满足实际测量需求。

融合 ＲＳＦ模型及边缘检测 ＬＯＧ算子图像分割

李老师的rsf模型的python代码Intensity inhomogeneities often occur in real-world images and may cause considerable difficulties in image segmenta- tion. In order to overcome the difficulties caused by intensity inho- mogeneities, we propose a region-based active contour model that draws upon intensity information in local regions at a controllable scale. A data fitting energy is defined in terms of a contour and two fitting functions that locally approximate the image intensities on the two sides of the contour. This energy is then incorporated into a variational level set formulation with a level set regularization term, from which a curve evolution equation is derived for energy minimization. Due to a kernel function in the data fitting term, in- tensityinformation inlocal regions isextracted toguidethemotion of the contour, which thereby enables our model to cope with in- tensity inhomogeneity. In addition, the regularity of the level set function is intrinsically preserved by the level set regularization term to ensure accurate computation and avoids expensive reini- tialization of the evolving level set function. Experimental results for synthetic and real images show desirable performances of our method.

见博客http://blog.csdn.net/dingkeyanlail/article/details/78818386