Abstract
Cataract leads to visual impairment. Among different types of cataract, posterior subcapsular cataract (PSC) can develop rapidly and surgery is usually needed. An approach to detect PSC opacities in retro-illumination images is proposed. Watershed and Markov random fields (MRF) method are employed to opacities in anterior retro-illumination images. It results in a mixture of PSC, cortical opacities and noise. Then, information in both anterior and posterior retro-illumination images is utilized. Two features are extracted to identify PSC: mean gradient comparison (MGC) between anterior and posterior retro-illumination images, and spatial location. This is the first time that comparison between anterior and posterior retro-illumination images is proposed and MGC is proposed as the feature of comparison in PSC detection. Experiments show that the sensitivity and specificity of PSC screening is 91.2 and 90.1%, respectively, based on the 519 pairs of testing images. To the best of our knowledge, it is the best performance reported in automatic detection of PSC. Compared with the methods in the literatures, considerable improvement is achieved when there are large areas of PSC opacities.
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