An Algorithm of Lung Cancer CT Image Segmentation Based on
Artificial Neural Network Model
SHI Hai1, YANG Fan2, HUANG Jiahai3, ZHOU Jie1
1. Department of Radiology, The 1st Affiliated Hospital of Nanjing Medical University, Nanjing Jiangsu 210029, China;
2. Department of Radiology, Taikang Xianlin Drum Tower Hospital, Nanjing Jiangsu 210000, China;
3. Department of Radiology, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Jiangsu 215001, China
Abstract:Objective To develop a lung cancer CT image segmentation algorithm based on artificial neural network model. Methods The
first step was to use Wiener filter and fuzzy enhancement to suppress image noise and enhance image contrast. The second step was
to extract texture and fractal features of the image. The third step was to train and test the artificial neural network model according to
the best parameters of the network. The fourth step was to extract the lung cancer lesion area in CT image. A total of 512 samples and
80 images were used to train and test the model. Results CT images of lung cancer contained 13 significant regional features (including
3 texture features and 10 fractal features). The best classification function obtained from training and testing data was Levenberg-Marquart
back propagation. The learning rate R was 0.3, the momentum was 0.9, and the number of hidden neurons was 20. The training
sensitivity, specificity and accuracy were 98.4%, 100% and 98.6%, respectively. The corresponding indicators in the test stage could
reach 90.9%, 100% and 95.1%, respectively. Conclusion Image segmentation algorithm based on artificial neural network model can
extract lung cancer lesions efficiently and accurately, and can be used as an effective tool for diagnosing lung cancer.
石海1,杨凡2,黄嘉海3,周洁1. 基于人工神经网络模型的肺癌CT图像分割算法[J]. 中国医疗设备, 2019, 34(10): 86-89.
SHI Hai1, YANG Fan2, HUANG Jiahai3, ZHOU Jie1. An Algorithm of Lung Cancer CT Image Segmentation Based on
Artificial Neural Network Model. China Medical Devices, 2019, 34(10): 86-89.