OPEN IN READ APP
JOURNAL ARTICLE

Retinal image analysis based on mixture models to detect hard exudates

Clara I Sánchez, María García, Agustín Mayo, María I López, Roberto Hornero
Medical Image Analysis 2009, 13 (4): 650-8
19539518
Diabetic Retinopathy is one of the leading causes of blindness in developed countries. Hard exudates have been found to be one of the most prevalent earliest clinical signs of retinopathy. Thus, automatic detection of hard exudates from retinal images is clinically significant. In this study, an automatic method to detect hard exudates is proposed. The algorithm is based on mixture models to dynamically threshold the images in order to separate exudates from background. A postprocessing technique, based on edge detection, is applied to distinguish hard exudates from cotton wool spots and other artefacts. We prospectively assessed the algorithm performance using a database of 80 retinal images with variable colour, brightness, and quality. The algorithm obtained a sensitivity of 90.2% and a positive predictive value of 96.8% using a lesion-based criterion. The image-based classification accuracy is also evaluated obtaining a sensitivity of 100% and a specificity of 90%.

Discussion

You are not logged in. Sign Up or Log In to join the discussion.

Related Papers

Available on the App Store

Available on the Play Store
Remove bar
Read by QxMD icon Read
19539518
×

Search Tips

Use Boolean operators: AND/OR

diabetic AND foot
diabetes OR diabetic

Exclude a word using the 'minus' sign

Virchow -triad

Use Parentheses

water AND (cup OR glass)

Add an asterisk (*) at end of a word to include word stems

Neuro* will search for Neurology, Neuroscientist, Neurological, and so on

Use quotes to search for an exact phrase

"primary prevention of cancer"
(heart or cardiac or cardio*) AND arrest -"American Heart Association"