Feng Changli, Zhang Jianxun, Liang Rui
Department of Information Science and Technology, Taishan University, Taian 271021, China ; Tianjin Key Laboratory of Intelligent Robotics, Institute of Robotics and Automatic Information System, College of Computer and Control Engineering, Nankai University, No. 94 Weijin Road, Tianjin 300071, China.
Tianjin Key Laboratory of Intelligent Robotics, Institute of Robotics and Automatic Information System, College of Computer and Control Engineering, Nankai University, No. 94 Weijin Road, Tianjin 300071, China.
Comput Math Methods Med. 2015;2015:789485. doi: 10.1155/2015/789485. Epub 2015 May 18.
In order to get the extracted lung region from CT images more accurately, a model that contains lung region extraction and edge boundary correction is proposed. Firstly, a new edge detection function is presented with the help of the classic structure tensor theory. Secondly, the initial lung mask is automatically extracted by an improved active contour model which combines the global intensity information, local intensity information, the new edge information, and an adaptive weight. It is worth noting that the objective function of the improved model is converted to a convex model, which makes the proposed model get the global minimum. Then, the central airway was excluded according to the spatial context messages and the position relationship between every segmented region and the rib. Thirdly, a mesh and the fractal theory are used to detect the boundary that surrounds the juxtapleural nodule. Finally, the geometric active contour model is employed to correct the detected boundary and reinclude juxtapleural nodules. We also evaluated the performance of the proposed segmentation and correction model by comparing with their popular counterparts. Efficient computing capability and robustness property prove that our model can correct the lung boundary reliably and reproducibly.
为了更准确地从CT图像中提取肺部区域，提出了一种包含肺部区域提取和边缘边界校正的模型。首先，借助经典结构张量理论提出了一种新的边缘检测函数。其次，通过改进的主动轮廓模型自动提取初始肺掩码，该模型结合了全局强度信息、局部强度信息、新的边缘信息和自适应权重。值得注意的是，改进模型的目标函数被转换为凸模型，这使得所提出的模型能够获得全局最小值。然后，根据空间上下文信息以及每个分割区域与肋骨之间的位置关系排除中央气道。第三，使用网格和分形理论检测围绕胸膜下结节的边界。最后，采用几何主动轮廓模型校正检测到的边界并重新纳入胸膜下结节。我们还通过与流行的同类模型进行比较，评估了所提出的分割和校正模型的性能。高效的计算能力和鲁棒性证明我们的模型能够可靠且可重复地校正肺部边界。
