Kalaie Soodeh, Gooya Ali
Department of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran.
Department of Electronic and Electrical Engineering, University of Sheffield, Sheffield, UK.
Comput Methods Programs Biomed. 2017 Nov;151:139-149. doi: 10.1016/j.cmpb.2017.08.018. Epub 2017 Aug 24.
Retinal vascular tree extraction plays an important role in computer-aided diagnosis and surgical operations. Junction point detection and classification provide useful information about the structure of the vascular network, facilitating objective analysis of retinal diseases.
In this study, we present a new machine learning algorithm for joint classification and tracking of retinal blood vessels. Our method is based on a hierarchical probabilistic framework, where the local intensity cross sections are classified as either junction or vessel points. Gaussian basis functions are used for intensity interpolation, and the corresponding linear coefficients are assumed to be samples from class-specific Gamma distributions. Hence, a directed Probabilistic Graphical Model (PGM) is proposed and the hyperparameters are estimated using a Maximum Likelihood (ML) solution based on Laplace approximation.
The performance of proposed method is evaluated using precision and recall rates on the REVIEW database. Our experiments show the proposed approach reaches promising results in bifurcation point detection and classification, achieving 88.67% precision and 88.67% recall rates.
This technique results in a classifier with high precision and recall when comparing it with Xu's method.
视网膜血管树提取在计算机辅助诊断和外科手术中发挥着重要作用。节点检测与分类为血管网络结构提供了有用信息，有助于对视网膜疾病进行客观分析。
在本研究中，我们提出了一种用于视网膜血管联合分类与跟踪的新机器学习算法。我们的方法基于分层概率框架，其中局部强度横截面被分类为节点或血管点。高斯基函数用于强度插值，并且相应的线性系数被假定为来自特定类别的伽马分布的样本。因此，提出了一种有向概率图模型（PGM），并使用基于拉普拉斯近似的最大似然（ML）解来估计超参数。
使用REVIEW数据库上的精确率和召回率对所提出方法的性能进行评估。我们的实验表明，所提出的方法在分叉点检测与分类方面取得了有前景的结果，精确率达到88.67%，召回率达到88.67%。
与徐氏方法相比，该技术产生了一个具有高精度和召回率的分类器。
