Fast, Accurate and Automatic Segmentation and Classification of Ophthalmic Optical Coherence Tomography Images Based on Sparse Representation

本申请中提出的研究方案针对基于稀疏表示的快速层析成像(OCT)图像。眼科图像的人工分析是一项耗时费力的主观过程。因此，近年来对这类图像的自动分析引起了人们的极大兴趣。用于从视网膜内获取详细图像的最新模式之一是OCT。OCT是眼科应用最快的诊断和研究视网膜病变的技术之一。将OCT技术与图像处理技术相结合，可以提供有关视网膜不同内层的详细信息，这些信息对于青光眼、糖尿病视网膜病变和老年性黄斑变性(AMD)等疾病的诊断至关重要，AMD是导致失明的三大主要原因。虽然已经做了大量有趣的工作来开发全自动/半自动OCT图像分析，但这些尝试通常仅限于对视网膜内层进行预处理或分割。这项研究的主要目标是引入一种新的建模方法，允许将OCT数据自动分类为4类：1)正常，2)青光眼，3)糖尿病视网膜病变，4)AMD。OCT数据自动分类的主要优势在于其在远程监控设备中的应用。远程监测将通过消除不必要的转介而减少看医生的次数，这反过来又改善了向实际需要特殊护理的人提供的护理。为了达到本研究的主要目的，申请人提出了一种原子表征模型。首先，找到最适合每一类的最佳原子(我们变换的基函数，也称为字典)。这些原子可以为OCT数据产生一个新的模型。原子表示的系数可用于分类目的。例如，对于正常数据，视网膜内的每一层都可以由其特定的独立原子表示；因此，正常数据可以通过这些原子的组合来重建。类似地，研究了AMD数据的一组不同的原子(因此也研究了不同的模型)，即AMD中提出的包囊原子与每一层的原子不同。在找到每个类别的最佳模型后，将获得的(稀疏)系数用于分类，而不是直接对图像进行分类。**

The research program proposed in this application aims at fast Tomography (OCT) images based on sparse representation. Manual analysis of ophthalmic images is a time and labor intensive subjective procedure. Therefore, in recent years automatic analysis of such images has gained a lot of interest. One of the more recent modalities used to obtain detailed images from within the retina is OCT. OCT is one of the fastest adopted technologies in ophthalmology for diagnosis and study of retinal pathologies. Combining OCT technologies with image processing techniques provides detail information about different internal layers of retina that are crucial for diagnosis of diseases such as glaucoma, diabetic retinopathy, and age-related macular degeneration (AMD) the three leading causes of blindness. Although a fair amount of interesting work has been done to develop fully/semi- automatic OCT image analysis, these attempts have been usually limited to preprocessing or segmentation of intra-retinal layers. The main goal of this research program is to introduce a novel modeling approach that allows automatic classification of OCT data into 4 categories: 1) normal, 2) glaucoma, 3) diabetic retinopathy, and 4) AMD. The main advantage of automatic classification of OCT data is in its application in remote monitoring devices. Remote monitoring will reduce doctor visits by eliminating unnecessary referrals which in turn improves the care provided to those who actually need special care. To reach the main goal of this research, the applicant proposes an atomic representation modeling. At first, the best atoms (basis functions of our transform, also known as dictionary) are found that fit best on each category. These atoms can produce a new model for the OCT data. The coefficients of the atomic representation can be used for classification purpose. For example, for normal data each intra-retinal layer can be represented by its specific independent atoms; therefore, the normal data can be reconstructed by a combination of these atoms. Similarly, a different set of atoms (and so a different model) for AMD data are investigated, i.e., the proposed atoms for cysts in AMD are different from each layer's atoms. After finding the best model for each category, the obtained (sparse) coefficients are used for classification instead of directly classifying the images.**