AS-OCT图像下的自动皮质性白内障分类框架

Automatic Cortical Cataract Classification Framework Based on AS-OCT Images

白内障是一种主要导致视觉损伤的眼病.早期干预和白内障手术是改善患者视力和生活质量的主要手段.眼前节光学相干断层成像图像(anterior segment optical coherence tomography,AS-OCT)是一种新型眼科图像,其具有非接触、高分辨率、检查快速等特点.在临床上,眼科医生已经逐渐采用AS-OCT图像进行眼科疾病如青光眼的诊断,然而尚未有研究工作利用它进行皮质性白内障(cortical cataract,CC)自动分类.为此,提出了一个基于ASOCT图像的自动皮质性白内障分类框架,由图像预处理、特征提取、特征筛选和分类等4部分组成.首先,利用反光区域去除和对比度增强方法进行图像预处理;紧接着使用灰度共生矩阵(grey level co-occurrence matrix,GLCM)、灰度区域大小矩阵(grey level size zone matrix,GLSZM)和邻域灰度差矩阵(neighborhood grey tone difference matrix,NGTDM)方法从皮质区域提取了22个特征;然后,采用斯皮尔曼相关系数方法对提取的特征进行特征重要性分析并筛除冗余特征;最后利用线性支持向量机方法进行分类.在一个临床AS-OCT图像数据集上的实验结果表明,所提出的皮质性白内障分类框架准确率、召回率、精确率和F1分别达到86.04%,86.18%,88.27%和86.35%,取得与先进的深度学习算法接近的性能,表明其具有作为辅助眼科医生进行皮质性白内障临床诊断工具的潜力.

Cataract is an ocular disease that mainly causes visual impairment and blindness,and early intervention and cataract surgery are the primary ways of improving the vision and the life quality of cataract patients.Anterior segment optical coherence tomography(AS-OCT)is a new type of ophthalmic image featuring non-contact,high resolution,and quick examination.In clinical practice,ophthalmologists have gradually used AS-OCT images to diagnose ophthalmic diseases such as glaucoma.However,none of the previous works have focused on automatic cortical cataract(CC)classification with such images.For this reason,this study proposes an automatic CC classification framework based on AS-OCT images,and it is composed of image preprocessing,feature extraction,feature screening,and classification.First,the reflective region removal and contrast enhancement methods are employed for image preprocessing.Next,22features are extracted from the cortical region by the gray level co-occurrence matrix(GLCM),grey level size zone matrix(GLSZM),and neighborhood gray-tone difference matrix(NGTDM)methods.Then,the Spearman correlation coefficient method is used to analyze the importance of the extracted features and screen out redundant ones.Finally,the linear support vector machine(linear-SVM)method is utilized for classification.The experimental results on a clinical AS-OCT image dataset show that the proposed CC classification framework achieves 86.04%accuracy,an 86.18%recall rate,88.27%precision,and 86.35%F1-score respectively and obtains performance comparable to that of the advanced deep learning-based algorithm,indicating that it has the potential to be used as a tool to assist ophthalmologists in clinical CC diagnosis.