Accurate vessel segmentation using maximum entropy incorporating line detection and phase-preserving denoising
Accurate vessel segmentation using maximum entropy incorporating line detection and phase-preserving denoising
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DOI：
10.1016/j.cviu.2016.12.005
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发表时间：
2017-02-01
影响因子：
4.5
通讯作者：
Zhang, Yanchun
中科院分区：
文献类型：
--
作者：
Pandey, Dinesh;Yin, Xiaoxia;Zhang, Yanchun
含有病变、渗出物、照明不均匀和病理伪影的视网膜图像存在细血管缺失和血管检测错误等固有问题。为了解决这些问题，我们提出了一种新颖的算法，该算法涉及背景图像的分离，以最大限度地减少噪声、不均匀照明和病变的影响。我们开发了两种不同的策略来分割细血管和粗血管。通过利用局部保相去噪、线检测、局部归一化和最大熵阈值来识别细血管。为了去除噪声并保留详细的血管信息，使用了保相去噪技术。该技术利用复数域中的log-Gabor小波响应来保留图像的相位信息。通过最大熵阈值提取和二值化厚血管。所提出算法的性能在四个流行数据库（DRIVE、STARE、CHASE_DB1、HRF）上进行了测试。结果表明，所提出的分割过程是自动、准确且计算高效的。 (C) 2016 Elsevier Inc. 保留所有权利。
The retinal images with lesions, exudates, non-uniformed illuminations and pathological artifacts have intrinsic problems such as the absence of thin vessels and false vessels detection. To solve these problems, we propose a novel algorithm which involves separation of background images to minimize the influence of noise, non-uniformed illuminations and lesions. We develop two different strategies to segment thin and thick blood vessels. Thin blood vessels are identified by taking benefits of local phase preserving denoising, line detection, local normalization and maximum entropy thresholding. To remove noise and preserve detailed blood vessels information, phase-preserving denoising technique is used. The technology takes an advantage of log-Gabor wavelet responses in the complex domain to preserve the phase information of the image. Thick vessels are extracted and binarized via maximum entropy thresholding. The performance of the proposed algorithm is tested on four popular databases (DRIVE, STARE, CHASE_ DB1, HRF). The results demonstrate that the proposed segmentation process is automatic, accurate and computationally efficient. (C) 2016 Elsevier Inc. All rights reserved.