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In this study, we proposed and validated a novel and accurate automatic approach for ulcer area extraction from ocular staining images. We first segmented the corneal surface area with the help of four pre-defined key landmarks by modeling the corneal surface shape as an ellipse. Then the ulcer area was identified within the cornea by employing a combination of techniques: 1) iterative k-means based clustering to extract areas with similar color information; 2) morphological operations to polish results from the previous step, with the parameters employed in the morphological operators determined automatically via linear regression analysis; 3) region growing to select the true ulcer area among a number of separated areas. To validate this automatic approach, we compared its results with those from manual delineations using the Dice Overlap Score (DSC) and the automatic-versus-manual correlation in terms of the ulcer area size based on 48 ocular staining images with corneal ulcers. The automatic results showed strong and statistically significant positive correlations with the manual ones in terms of both the cornea size and the ulcer area size (cornea: PCC=0.842, p-value=6:890 x 10-14; corneal ulcer area: PCC=0.969, p-value=1:119 x 10-29). For cornea, the DSC between the proposed automatic results and the manual ones is on average 0.989, whereas the average DSC for the ulcer segmentation is 0.879. This suggests a high overlap between the automatic and the manual results for both the cornea and the corneal ulcer area. We also compared the proposed method with a classic segmentation approach (the active contour). Our results revealed a superior performance of the proposed automatic approach in corneal ulcer area identification relative to the active contour (0.879 versus 0.639 in terms of DSC).
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