An Autonomous way to detect and quantify Cataracts using Computer Vision

Abstract

Our project is based on the detection of cataracts through python. A cataract is a clouding of the normally clear lens of the eye and the clouded vision caused by cataracts can increase difficulties in daily activities like reading and driving a car. Cataracts are the biggest cause of blindness in older people and data from the National Eye Institute (NEI) shows that over 65% of people of ages 80 and older are diagnosed with cataracts. However, there are limitations in the current method of cataract detection which include: Lack of clinical skill as cataracts are diagnosed by ophthalmologists by using slit-lamp bio microscopy and established clinical scales. This presents a barrier, specifically in rural areas where expert ophthalmologists are low in supply. Another limitation exists in the method used to calculate IOL power. Currently, the algorithm used for power calculation is unstandardized and at times, is at the surgeon's subjective judgment.

To address this problem, we have created a program through the use of python libraries which include OpenCV, NumPy, and FPDF. Our program takes the patient’s formalities and eye image as input and generates a pdf report, informing whether the patient is diagnosed with a cataract or not. If yes, the program also adds the percentage of the eye area covered by the cataract and the severity of the cataract to the pdf. The program creates colour masks, white for the cataract detection and other colors like brown and black, to account for different eye colours. We also added ranges to the color masks to check the intensity of the cataract. Using all the masks it detects the cataract and also extracts important information like whiteness, size of the cataract, and also its percentage in the eye. All this information helps us quantify the severity of the disease. The object report output of our solution can help doctors to easily detect the cataract and solve the issue.