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基于裂隙和血管的胸部Ct肺叶自动分割算法

摘要

肺实质的横叶检查具有重要的科学意义，对肺部疾病的诊断和病理监测具有重要意义。在医学应用中，肺叶分割对于特定患者的肺叶识别和治疗具有非常重要的意义。图像分割是将原始图像分割成均匀区域的过程，在医学图像处理中起着重要的作用。在肺叶分割方法中，肺部图像的分割是一个非常困难和具有挑战性的问题，因为不同的CT扫描仪具有不同的反射分辨率、噪声和特征。此外，检查差异多，不完全性骨折多发，特别是在严重的肺部疾病病例中。在最近的工作中，自动肺叶分割是基于分水岭变换进行分割，目的是获得肺叶气道和血管的检查沉迷于描述，但分割结果是基于欧几里得距离度量进行融合，结果较少，为了解决本工作中的这一问题。一种新型的自动肺叶分割方法应用于计算机断层扫描(CT)，将肺叶分割为肺裂、血管和体素。采用中真度度量(MMTD)进行肺叶的自动分割。MMTD测量原始像素之间的相似度，利用像素之间的相关性。在初始阶段，通过计算标记支气管树来确定肺叶标记。 In initial stage of the work the pulmonary vessels are detected based on MMTD. In the second step of the work pulmonary fissures is segmented based on the fissure enhancement where eigen values of is determined from Hessian matrix . In third stage two pre-processing steps is applied such as Gaussian smoothening and bronchial tree . Gaussian smoothening is mainly performed to reduce the noises in the input image samples. The bronchial tree is mainly applied to enhance the quality of the input image samples. Cost image is calculated through mixing the information of fissures, bronchi, and pulmonary vessels distance results. The experimentation results of the proposed MMTD- Lobar segmentation is compared with earlier methods and it applied for 20 CT scans through rejection or mild disease.

Poonkodi R, Geetharani M, Gunasekaran R

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