摘要
视网膜是层状结构,临床上可以根据视网膜层厚度改变对一些疾病进行预测和诊断.为了快速且准确地分割出视网膜的不同层带,本论文提出一种基于主成分分析的随机森林视网膜光学相干断层扫描技术(optical coherence tomography,OCT)图像分层算法.该方法使用主成分分析(principal component analysis,PCA)法对随机森林采集到的特征进行重采样,保留重采样后权重大的特征信息维度,从而消除特征维度间的关联性和信息冗余.结果表明,总特征维度在29维的情况下,保留前18维度训练速度提高了23.20%,14维度训练速度提高了42.38%,而对图像分割精度方面影响较小,实验表明该方法有效地提高了算法的效率.
The retina is a layered structure,and some diseases can be clinically predicted and diagnosed based on the change in the thickness of the retinal layer.To segment the different layers of the retina quickly and accurately,this study proposes a random forest algorithm based on principal component analysis(PCA).The algorithm uses PCA to resample the normalized features collected from the retinal images and retains the feature information dimensions with significant weight,thereby eliminating the relevance between the different feature dimensions and information redundancy.After PCA,the number of features can be reduced obviously,but still retains 99%information.Random forests algorithm applies the features to learn and predict the location of retinal layer boundaries.We extract each pixels values of retinal boundaries,producing an accurate probability map for each boundary.Experimental results show that when the total number of feature dimensions decreased from 29 to 18,the training speed of the model increased by 23.20%.By contrast,when the number of feature dimensions was 14,the training speed increased by 42.38%.However,the effect on image segmentation accuracy was not obvious.Thus,it is found that this method effectively improves the efficiency of the algorithm.
作者
李晓雯
王陆权
曾亚光
陈允照
王茗祎
钟俊平
王雪花
熊红莲
陈勇
LI Xiao-Wen;WANG Lu-Quan;ZENG Ya-Guang;CHEN Yun-Zhao;WANG Ming-Yi;ZHONG Jun-Ping;WANG Xue-Hua;XIONG Hong-Lian;CHEN Yong(Automatic College,Foshan University,Foshan528000,China;School of Physics and Optoelectronic Engineering,Foshan University,Foshan528000,China)
出处
《生物化学与生物物理进展》
SCIE
CAS
CSCD
北大核心
2021年第3期336-343,共8页
Progress In Biochemistry and Biophysics
基金
国家自然科学基金(81601534,61771139,61805038,61705036)
国家重点研发计划(2018YFC1406601)
广东省自然科学基金(2017A030313386)资助项目。
