基于边界逼近的肺实质分割方法

肺部CT图像的分割是计算机辅助诊断系统处理的一个重要环节,其分割的结果影响到医生的诊断与进一步的分析。由于胸膜结节的灰度与肺实质外围的灰度相近,运用传统的分割方法无法正确分割出此类病灶。将胸膜结节包含肺实质一起分割出来,使计算机辅助诊断系统能够对此类病灶做进一步的分析。提出一种结合Graham算法以及边界逼近的方法,对肺实质的轮廓进行修正,进而得到修正的二值模板;将该模板与原图像做乘运算,得到包含胸膜结节的肺实质。运用所提出的方法,对公开数据库LIDC中68张含病灶的CT样本图像做处理,通过与传统方法的对比以及对算法的过分割比例、欠分割比例以及准确性的分析,得到准确率为98.5%,平均过分割比例为1.35%,平均欠分割比例为0.51%,证明了该方法的有效性。

Lung CT Image Segmentation Based on Border Approximation

Segmentation of the lungs in chest-computed tomography (CT) image performs as an important preprocessing step in Computer-aided detection (CAD). The result brings a great effect for the further analysis and diagnosis. As the intensity of pleural nodule is close to the peripheral lung parenchyma, these lesions are not able to be segmented correctly using traditional method. The aim of this paper is to segment the lung including juxtapleural nodules in order to provide this focus for CAD system for the further analysis. This paper proposed a method that combined the Graham algorithm with border matching approximation to correct the contour of lung, and obtained the mask and multiply it by original image to segment lung image with juxtapleural nodules. Processing the 68 sample CT images including nodules from LIDC (Lung Image Database Consortium) through adopting new method proposed in this paper, and comparing this method with a traditional one, and analyzing the accuracy of this method and the rate of oversegmentation and undersegmentation, the accuracy of 98.5% , the oversegmentation rate of 1.35% and the undersegmentation of 0.51% were determined, which proved the effectiveness of this method.