Work

Semantic Reconstruction-Based Nuclear Cataract Grading from Slit-Lamp Lens Images

Year: 2016

Type: conference-paper

Abstract: Cataracts are the leading cause of visual impairment and blindness worldwide. Cataract grading, i.e. assessing the presence and severity of cataracts, is essential for diagnosis and progression monitoring. We present in this work an automatic method for predicting cataract grades from slit-lamp lens images. Different from existing techniques which normally formulate cataract grading as a regression problem, we solve it through reconstruction-based classification, which has been shown to yield higher performance when the available training data is densely distributed within the feature space. To heighten the effectiveness of this reconstruction-based approach, we introduce a new semantic feature representation that facilitates alignment of test and reference images, and include locality constraints on the linear reconstruction to reduce the influence of less relevant reference samples. In experiments on the large ACHIKO-NC database comprised of 5378 images, our system outperforms the state-of-the-art regression methods over a range of evaluation metrics. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Source: Lecture notes in computer science

DOI: 10.1007/978-3-319-46726-9_53

Authors: Yanwu Xu, Lixin Duan, Damon Wing Kee Wong, Tien Yin Wong, Jiang Liu