最佳阈值法结合改进的Freeman链码的肺实质分割

肺部CT图像中肺实质的精确分割是肺部疾病检测和诊断的关键步骤。针对传统的图像分割方法对CT图像中肺实质分割效果不理想,提出了一种基于最佳阈值法和改进的Freeman链码的肺实质分割方法。首先,用最佳阈值法实现肺部初分割,然后对肺实质进一步处理得到肺实质模板,再结合改进的Freeman链码法和Bezier曲线对存有缺陷的模板进行修补,最后与肺部CT图像相乘来提取肺实质。肺实质分割精度在图像对比清晰度和肺实质区域特征一致性方面都有一定的改善,分割准确率平均达到96.8%。实验结果表明,对于边缘性结节以及不同的肺部病灶,该方法都具有理想的分割效果,具有较好的准确性和鲁棒性。

Combining the optimal threshold method and the improved Freeman chain code for lung parenchyma segmentation

Accurate segmentation of lung parenchyma in lung CT images is a key step in the detection and diagnosis of lung diseases. In view of the fact that the traditional image segmentation method is not ideal for lung parenchyma segmentation in CT images, a lung parenchyma segmentation method based on optimal threshold method and improved Freeman chain code is proposed. Firstly, the optimal threshold method is used to realize the initial segmentation of lung parenchyma, then the lung parenchyma template is further processed. Secondly, the defective template is repaired by the improved Freeman chain code method and Bezier curve. Finally, the lung parenchyma is extracted by its multiplication with CT images of the lung. The accuracy of lung parenchyma segmentation is improved in image contrast clarity and consistency of lung parenchyma features, and the average segmentation accuracy can reach 96.8%. The experimental results show that, for marginal nodules and different lung lesions, this method has ideal segmentation effect and has good accuracy and robustness.