一种基于改进分水岭算法的PET/CT检测肺结节的新方法。
A new method of detecting pulmonary nodules with PET/CT based on an improved watershed algorithm.
作者信息
Zhao Juanjuan, Ji Guohua, Qiang Yan, Han Xiaohong, Pei Bo, Shi Zhenghao
机构信息
College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China.
College of Computer Science and Engineering, Xi'an University of Technology, Xi'an 710048, China.
出版信息
PLoS One. 2015 Apr 8;10(4):e0123694. doi: 10.1371/journal.pone.0123694. eCollection 2015.
BACKGROUND
Integrated 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) is widely performed for staging solitary pulmonary nodules (SPNs). However, the diagnostic efficacy of SPNs based on PET/CT is not optimal. Here, we propose a method of detection based on PET/CT that can differentiate malignant and benign SPNs with few false-positives.
METHOD
Our proposed method combines the features of positron-emission tomography (PET) and computed tomography (CT). A dynamic threshold segmentation method was used to identify lung parenchyma in CT images and suspicious areas in PET images. Then, an improved watershed method was used to mark suspicious areas on the CT image. Next, the support vector machine (SVM) method was used to classify SPNs based on textural features of CT images and metabolic features of PET images to validate the proposed method.
RESULTS
Our proposed method was more efficient than traditional methods and methods based on the CT or PET features alone (sensitivity 95.6%; average of 2.9 false positives per scan).
背景
集成18F-氟脱氧葡萄糖正电子发射断层扫描/计算机断层扫描（18F-FDG PET/CT）广泛用于孤立性肺结节（SPN）的分期。然而，基于PET/CT的SPN诊断效能并不理想。在此，我们提出一种基于PET/CT的检测方法，该方法能够以较少的假阳性区分恶性和良性SPN。
方法
我们提出的方法结合了正电子发射断层扫描（PET）和计算机断层扫描（CT）的特征。采用动态阈值分割方法识别CT图像中的肺实质和PET图像中的可疑区域。然后，使用改进的分水岭方法在CT图像上标记可疑区域。接下来，基于CT图像的纹理特征和PET图像的代谢特征，采用支持向量机（SVM）方法对SPN进行分类，以验证所提出的方法。
结果
我们提出的方法比传统方法以及仅基于CT或PET特征的方法更有效（灵敏度95.6%；每次扫描平均2.9例假阳性）。
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