Automatic Grading of Cortical and PSC Cataracts Using Retroillumination Lens Images

In this paper, we propose an automatic approach to grade cortical and Posterior Sub-Capsular (PSC) cataracts using retroillumination images. Low-level vision features are used to characterize the photometric appearances and geometric structures of cortical and PSC cataracts in retroillumination images. The prediction result gives an opacity score that serves as an estimation of cataract severity. The system is tested on 434 pairs of lens images with ground truth labels from professional graders. Five-fold cross-validation with random partition of the data shows that the mean correlation between the proposed method and the grader's result is 0.7392 with variance 0.0003, which is promising. The proposed prediction approach can also be used as a preliminary estimation to improve existing detection systems. Most existing detection systems apply one method to all types of lens images. Such single method may fail for one to some types of lens images as cataracts with different severity not only have different levels of opacity, but also have different photometric appearances and geometric structures. We demonstrate an improved cortical cataracts detection system that employs different strategies to address the challenges in cataract detection for lenses with different levels of estimated opacity. The strategies simultaneously overcome the over-detection issue for clear lenses and the under-detection issue for lenses with high opacity. The results show an improvement of accuracy in cortical cataract detection from 51% to 62%. The corresponding Kappa agreement score is improved from 0.25 to 0.40.