PURPOSE: To describe a software program developed to provide an objective assessment of the amount of posterior capsular opacification (PCO) in high-resolution digital images of the posterior capsule after cataract surgery. METHODS: Images are analyzed by a set protocol of defining the area of the posterior capsule, removing the Purkinje light reflexes by intensity segmentation, contrast enhancement, filtering to enhance low-density PCO, and variance analysis using a co-occurrence matrix to assess texture. The accuracy of the system was tested for validity and repeatability. RESULTS: The software developed has been demonstrated to be an objective method of quantifying PCO. In validation tests, the image analysis-derived measure of PCO showed good agreement with clinically derived measures of PCO. Clinicians assessed PCO on a computer screen image and also under slit lamp examination (Pearson correlation coefficient for both methods >0.92). The entire acquisition and analysis system was demonstrated to have a confidence limit for 2 SDs of 9.8% for group data. CONCLUSIONS: This system is capable of producing an accurate and reproducible measure of PCO that is relevant to assessing techniques of PCO prevention.
PURPOSE: To describe a software program developed to provide an objective assessment of the amount of posterior capsular opacification (PCO) in high-resolution digital images of the posterior capsule after cataract surgery. METHODS: Images are analyzed by a set protocol of defining the area of the posterior capsule, removing the Purkinje light reflexes by intensity segmentation, contrast enhancement, filtering to enhance low-density PCO, and variance analysis using a co-occurrence matrix to assess texture. The accuracy of the system was tested for validity and repeatability. RESULTS: The software developed has been demonstrated to be an objective method of quantifying PCO. In validation tests, the image analysis-derived measure of PCO showed good agreement with clinically derived measures of PCO. Clinicians assessed PCO on a computer screen image and also under slit lamp examination (Pearson correlation coefficient for both methods >0.92). The entire acquisition and analysis system was demonstrated to have a confidence limit for 2 SDs of 9.8% for group data. CONCLUSIONS: This system is capable of producing an accurate and reproducible measure of PCO that is relevant to assessing techniques of PCO prevention.