基于三维曲面重建的CT图像肺边界缺陷修复

针对CT图像的肺实质分割中由边界粘连型肿瘤造成的肺边界缺陷修复问题,提出了一种基于三维曲面重建的修复方法.对肺实质边界曲率变化较大处的缺陷,二维图像上无法获得足够多的特征对肺实质边界进行修复.本文方法首先使用质心灰度法改进了三维区域生长算法,提取肺实质进行三维重建.再使用阈值法提取分布在缺陷周围的三维点云,对三维点云进行曲面重建即可得到完整的肺实质轮廓.实验结果表明:该方法与传统的凸包算法和滚球法相比,能够更加完整有效地修复边界粘连型肺实质边界的缺陷.

胸部CT图像肺区域边界凹陷自动修补

提出用边界曲线局部极小值点连线法修补阈值分割后的CT横断面图像肺区域边界处血管和胸膜结节型凹陷。设置不同的匹配模板,实现在不同坐标系下寻找边界曲线的局部极小值点,从而确定需要修补的位置。将肺区域边界线上的点分为局部极小值点和非局部极小值点两类,连接凹陷缺口处两边的两个邻近局部极小值点,修补肺边界凹陷部分。实验表明,与通过计算边界点的曲率找边界凸点方法相比,该方法不仅有效地降低了肺实质分割的计算量,而且减少了由于过度分割造成的分割错误。

An advanced segmentation using area and boundary tracing technique in extraction of lungs region

A new method is presented for the segmentation of pulmonary parenchyma. The proposed method is based on the area calculation of different objects in the image. The main purpose of the proposed algorithm is the segment of the lungs images from the computer tomography(CT) images. The original image is binarized using the bit-plane slicing technique and among the different images the best binarized image is chosen. After binarization, the labeling is done and the area of each label is calculated from which the next level of binarized image is obtained. Then, the boundary tracing algorithm is applied to get another level of binarized image. The proposed method is able to extract lung region from the original images. The experimental results show the significance of the proposed method.