基于眼前节相干光断层扫描成像的核性白内障分类算法

白内障是导致视觉损害和致盲的主要眼病,眼前节光学相干断层成像技术(Anterior Segment Optical Coherence Tomography,AS-OCT)具有非接触、高分辨率、检查快速、客观定量化测量等特点,在临床上已经被广泛应用于眼病的诊断.目前缺乏基于眼前节OCT图像的核性白内障分类研究工作,为此...

Classification Algorithm of Nuclear Cataract Based on Anterior Segment Coherence Tomography Image

Cataract is a main ocular disease for visual impairment and blindness.Anterior segment optical coherence tomography(AS-OCT)technique has the characteristics of non-invasiveness,high resolution,rapid inspection,and objective quantitative measurement.AS-OCT images have been widely used for the diagnosis of ocular diseases in clinical ophthalmology.Inthecurrent,it is lack of the research on classification methods of nuclear cataract based on AS-OCT images.To this end,this paper proposes a nuclear cataract classification method based on AS-OCT images.First,the nucleus region of the lens is extracted from AS-OCT images using a combination of adaptive threshold method,edge detection Canny algorithm and manual correction pattern.Then,eighteen pixel features are extracted based on image intensity and histogram feature statistical methods,and the Pearson correlation coefficient method is used to analyze the correlation between the extracted pixel features and the severity of nuclear cataract.Finally,the random forest algorithm is used to build a classification model for getting nuclear cataract classification results.Experimental results on an AS-OCT image dataset show that the proposed method achieves the accuracy and recall with 75.53%and 74.04%respectively.Therefore,the proposed method has the potential as a quantitative analysis reference tool for the clinical diagnosis of nuclear cataract.