Unsupervised Anomaly Detection for Glaucoma Diagnosis

Abstract

With the rapid development of high tech, Internet of Things (IoT) and artificial intelligence (AI) achieve a series of achievements in the healthcare industry. Among them, automatic glaucoma diagnosis is one of them. Glaucoma is second leading cause of blindness in the world. Although many automatic glaucoma diagnosis approaches have been proposed, they still face the following two challenges. First, the data acquisition of diseased images is extremely expensive, especially for disease with low occurrence, leading to the class imbalance. Second, large-scale labeled data are hard to obtain in medical image domain. The aforementioned challenges limit the practical application of these approaches in glaucoma diagnosis. To address these disadvantages, this paper proposes an unsupervised anomaly detection framework based on sparse principal component analysis (SPCA) for glaucoma diagnosis. In the proposed approach, we just employ the one-class normal (nonglaucoma) images for training, so the class imbalance problem can be avoided. Then, to distinguish the glaucoma (abnormal) images from the normal images, a feature set consisting of segmentation-based features and image-based features is extracted, which can capture the shape and textural changes. Next, SPCA is adopted to select the effective features from the feature set. Finally, with the usage of the extracted effective features, glaucoma diagnosis can be automatically accomplished via introducing the $T^2$ statistic and the control limit, overcoming the issue of insufficient labeled samples. Extensive experiments are carried out on the two public databases, and the experimental results verify the effectiveness of the proposed approach.