计算机科学 ›› 2017, Vol. 44 ›› Issue (8): 296-300.doi: 10.11896/j.issn.1002-137X.2017.08.051
所属专题： 医学图像
廖晓磊,赵涓涓
LIAO Xiao-lei and ZHAO Juan-juan
摘要： 针对肺实质序列图像分割方法的时效性低和分割不完全性等问题,利用先验知识得到肺部CT序列ROI图像,提出超像素序列分割算法对ROI序列图像进行分割,采用改进的自生成神经网络对超像素进行聚类并优化,根据聚类后样本的灰度和位置特征识别肺实质区域。在序列肺实质图像的分割结果中,单张CT图像的平均处理时间为0.61s,同时能达到92.09±1.52%的平均肺部体素重合度。与已有的方法相比,所提算法能在相对较短的时间内获得较高的分割精准度。
| [1] SIEGEL R,NAISHADHAM D,J EMAL A.Cancer statistics,2012[J].A Cancer Journal for Clinicians,2012,62(1):10-29. [2] ANTONELLI M,FROSINI G,LAZZERINI B,et al.Automated Detection of Pulmonary Nodules in CT Scans[C]∥International Conference on Computational Intelligence for Modelling,Control and Automation and International Conference on Intelligent Agents,Web Technologies and Internet Commerce Vol.IEEE Computer Society,2005:799-803. [3] GILL G,TOEWS M,BEICHEL R R.Robust Initialization of Active Shape Models for Lung Segmentation in CT Scans:A Feature-Based Atlas Approach[J].International Journal of Biomedical Imaging,2014,2014(1/2):479154-479154. [4] CAO L,ZHAN J,YU X E,et al.Fast lung segmentation algorithm for thoracic CT based on automated thresholding[J].Computer Engineering and Applications,2008,4(12):178-181.(in Chinese) 曹蕾,占杰,余晓锷,等.基于自动阈值的CT图像快速肺实质分割算法[J].计算机工程与应用,2008,44(12):178-181. [5] MANSOOR A,BAGCI U,MOLLURA D J.Near-optimal keypoint sampling for fast pathological lung segmentation[C]∥2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).IEEE,2014:6032-6035. [6] SHOJAII R,ALIREZAIE J,BABYN P.Automatic segmenta-tion of abnormal lung parenchyma utilizing wavelet transform[C]∥IEEE International Conference on Acoustics,Speech and Signal Processing,2007(ICASSP 2007).IEEE,2007:1217-1220. [7] QIANG Y,ZHANG X,JI G,et al.Automated Lung Nodule Segmentation Using an Active Contour Model Based on PETCT Images[J].Journal of Computational and Theoretical Nanoscience,2015,12(8):1972-1976. [8] REN X,MALIK J.Learning a classification model for segmentation[C]∥Ninth IEEE International Conference on Computer Vision,2003.IEEE,2003:10-17. [9] WEN W X,LIU H,JENNINGS A.Self-generating neural networks[C]∥International Joint Conference on Neural Networks.IEEE,1992:850-855. [10] ZHAO J,JI G,XIA Y,et al.Cavitary nodule segmentation in computed tomography images based on self-generating neural networks and particle swarm optimisation[J].International Journal of Bio-Inspired Computation,2015,7(1):62-67. [11] WU H,WANG R G,FANG S,et al.Image segmentation algorithm research based on minimum within-clusterdifference and maximum between-cluster difference[J].Journal of Engineering Graphics,2011,2(1):67-75.(in Chinese) 吴昊,汪荣贵,方帅,等.基于最小类内差和最大类间差的图像分割算法研究[J].图学学报,2011,32(1):67-75. [12] LI L.Optimized self-generating neural network for image segmentation[J].Infrared Technology,2010,32(4):198-203.(in Chinese) 李露.SGNN优化算法的研究及其在图像分割中的应用[J].红外技术,2010,32(4):198-203. [13] JI G H,ZHAO J J,PAN L.Classification of solitary pulmonary nodules based on improved self-generating neural network[J].Journal of Taiyuan University of Technology,2015,6(6):754-759.(in Chinese) 纪国华,赵涓涓,潘玲.基于改进自生成神经网络的孤立性肺结节分类[J].太原理工大学学报,2015,46(6):754-759. [14] LI D W,WANG J,CHEN J H,et al.Liver segmentation by using an optimal framework for CT images[J].Chinese Journal of Computers,2016,9(7):1-13.(in Chinese) 李登旺,王杰,陈进琥,等.基于最优值搜索框架的CT序列图像肝脏分割[J].计算机学报,2016,9(7):1-13. [15] UNNIKRISHNAN R,HEBERT M.Measures of similarity[C]∥Seventh IEEE Workshops on Application of Computer Vision,2005(WACV/MOTIONS’05).IEEE,2005,1:394. |
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