School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin, 541004, China.
School of Electronic Information and Automation, Guilin University of Aerospace Technology, Guilin, 541004, China.
Biomed Eng Online. 2020 Jun 17;19(1):51. doi: 10.1186/s12938-020-00793-0.
Image segmentation is an important part of computer-aided diagnosis (CAD), the segmentation of small ground glass opacity (GGO) pulmonary nodules is beneficial for the early detection of lung cancer. For the segmentation of small GGO pulmonary nodules, an integrated active contour model based on Markov random field energy and Bayesian probability difference (IACM_MRFEBPD) is proposed in this paper.
First, the Markov random field (MRF) is constructed on the computed tomography (CT) images, then the MRF energy is calculated. The MRF energy is used to construct the region term. It can not only enhance the contrast between pulmonary nodule and the background region, but also solve the problem of intensity inhomogeneity using local spatial correlation information between neighboring pixels in the image. Second, the Gaussian mixture model is used to establish the probability model of the image, and the model parameters are estimated by the expectation maximization (EM) algorithm. So the Bayesian posterior probability difference of each pixel can be calculated. The probability difference is used to construct the boundary detection term, which is 0 at the boundary. Therefore, the blurred boundary problem can be solved. Finally, under the framework of the level set, the integrated active contour model is constructed.
To verify the effectiveness of the proposed method, the public data of the lung image database consortium and image database resource initiative (LIDC-IDRI) and the clinical data of the Affiliated Jiangmen Hospital of Sun Yat-sen University are used to perform experiments, and the intersection over union (IOU) score is used to evaluate the segmentation methods. Compared with other methods, the proposed method achieves the best results with the highest average IOU of 0.7444, 0.7503, and 0.7450 for LIDC-IDRI test set, clinical test set, and all test sets, respectively.
The experiment results show that the proposed method can segment various small GGO pulmonary nodules more accurately and robustly, which is helpful for the accurate evaluation of medical imaging.
图像分割是计算机辅助诊断（CAD）的重要组成部分，对磨玻璃密度（GGO）肺小结节的分割有利于肺癌的早期发现。针对小 GGO 肺结节的分割问题，提出了一种基于马尔可夫随机场能量和贝叶斯概率差的综合主动轮廓模型（IACM_MRFEBPD）。
首先在 CT 图像上构建马尔可夫随机场（MRF），然后计算 MRF 能量。MRF 能量用于构建区域项，不仅可以增强肺结节与背景区域之间的对比度，还可以利用图像中相邻像素之间的局部空间相关性信息解决强度不均匀性问题。其次，使用高斯混合模型建立图像的概率模型，并通过期望最大化（EM）算法估计模型参数，从而计算出每个像素的贝叶斯后验概率差。概率差用于构建边界检测项，在边界处为 0，从而解决边界模糊问题。最后，在水平集框架下构建综合主动轮廓模型。
为了验证所提方法的有效性，使用肺图像数据库联盟和图像数据库资源倡议（LIDC-IDRI）的公共数据集以及中山大学附属江门医院的临床数据进行实验，并用交并比（IOU）得分来评估分割方法。与其他方法相比，所提方法在 LIDC-IDRI 测试集、临床测试集和所有测试集上的平均 IOU 得分最高，分别为 0.7444、0.7503 和 0.7450，取得了最好的结果。
实验结果表明，该方法能够更准确、稳健地分割各种小 GGO 肺结节，有助于对医学图像进行准确评估。
