Automated post-trabeculectomy bleb assesment by using image processing

Glaucoma is a second leading cause of blindness after cataract. Glaucoma caused by unbalance absorption of aqueus humour so it increase intraocular pressure. As a result, it surpresses nerve cells so that nerve cells can not get enough blood flow as nutrition intake and can lead to permanent blindness. One of the treatment for glaucoma is by surgical procedure, called trabeculectomy. After the surgery a slightly lifted tissue due to passing fluid, called bleb, should appears. Bleb assesment is necessary to examine the successful of trabeculectomy surgery. One of standard assesment is Indiana Bleb Appearance Grading Scale (IBAGS). Ophthalmologist used this standard to grade the bleb images manually so the result is subjective. This work offered a new approach to standardize the system of bleb assessment by computer software. Features related to bleb height, width and vascularity were extracted from the bleb image by using image processing algorithm. The KNN algorithm then used to classify the image according the IBAGS. The proposed method has successfully increased the Cohen's kappa coefficient from 0.56 to 0.63. Therefore, it potentially reduced the subjectivity of the bleb grading.