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首页> 外文会议>ICME International Conference on Complex Medical Engineering >Vessel segmentation in 2-D optical coherence tomography images
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Vessel segmentation in 2-D optical coherence tomography images

机译：2-D光学相干断层扫描图像中的血管分割

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This paper described a novel region segmentation method to avoid difficulties of the threshold process used in traditional segmentation methods in 2-D optical coherence tomography (OCT) images. The speckle effect and diffusion problems make traditional image processing methods such as Canny edge and Otsu methods fail on finding layers and region edges in OCT images. The overcomplete-wavelet-frame-based fractal signature method based on high-pass information and a fuzzy-c-mean algorithm is considered to avoid the threshold processing, but the high-pass information is distorted because of noises and diffusions. To improve the high-pass information distortion problem, the proposed method uses the mean value and an enhanced-fuzzy-c-mean algorithm to cluster pixels in 2-D OCT images and find the edge between different clustered regions. The vessel OCT images are tested in the experiment, and the experimental results show that the proposed method performs with more accurate segmentation results than the overcomplete-wavelet-frame-based fractal signature method.
机译：本文描述了一种新颖的区域分割方法，以避免在2-D光学相干断层扫描（OCT）图像中传统分段方法中使用的阈值过程的困难。散斑效果和扩散问题使传统的图像处理方法如Canny Edge和Otsu方法在OCT图像中查找层和区域边缘失败。基于高通信息和模糊-C平均算法的基于过度通信的小波帧的分形签名方法被认为是避免阈值处理，但是由于噪声和扩散而失真了高通信息。为了提高高通信息失真问题，所提出的方法使用平均值和增强模糊-C平均算法在2-D OCT图像中的群集像素，并在不同聚类区域之间找到边缘。在实验中测试了血管OCT图像，实验结果表明，该方法的性分割结果比超可达 - 小波框架的分形签名方法更精确地进行。

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