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Computer Science ›› 2018, Vol. 45 ›› Issue (6A): 187-192.

• Pattern Recognition & Image Processing • Previous Articles     Next Articles

SD-OCT CSC NRD Region Segmentation Based on Region Restricted 3D Region Growing

HE Xiao-jun1,WU Meng-lin2,FAN Wen3,YUAN Song-tao3,CHEN Qiang1,4   

  1. School of Computer Science and Engineering,Nanjing University of Science and Technology,Nanjing 210094,China1
    College of Electronics and Information Engineering,Nanjing University of Technology,Nanjing 211816,China2
    Dartment of Ophthalmology,The First Affiliated Hospital with Nanjing Medical University,Nanjing 210029,China3
    Fujian Provincial Key Laboratory of Information Processing and Intelligent Control,Minjiang University,Fuzhou 350121,China4
  • Online:2018-06-20 Published:2018-08-03

Abstract: It is important to segment neurosensory retianl detachment (NRD) of central serous chorioretinopathy (CSC) region,because the volume of CSC region plays a very important role in the diagnosis and study of CSC,while NRD is the most common and serious situation in CSC.The paper presented an automated spatial-domain optical cohe-rence tomography (SD-OCT) NRD segmentation method,which firstly segments NRD lesion in 3D space.And the segmentation of lesion in two-dimensional images is transformed into three-dimensional space segmentation problem,which makes full use of the three-dimensional structure information of data and improves the segmentation precision.The experiment results with 18 SD-OCT cubes indicate that the proposed method can segment the NRD accurately,and the average area coverage is as high as 89.5%.Compared to other four segmentation methods,the proposed algorithm achieves the highest accuracy and costs the least time,which has great advantages in clinical application and research.

Key words: 3-D region growing, Adaptive threshold, Central serous chorioretinopathy, Neurosensory retianl detachment, Spatial-domain optical coherence tomography

CLC Number: 

  • TP391
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