Taking part in a transformative project in the field of ophthalmology, I developed OptiQual, an automated pupil image analysis system. This application plays a crucial role in ensuring the quality of images taken with a Fundus Camera, a device used by doctors for internal inspection of the eye. High-quality images are pivotal in accurate diagnosis and treatment, which makes OptiQual a significant advancement in the patient examination process.
Often, images that don't meet specific quality criteria are rejected by doctors, requiring patients to return for another examination. OptiQual tackles this problem by performing a multitude of validation checks on the captured images, ensuring they are up to the required standard. It verifies image brightness, dust particles, overall quality, blurriness, and dirt, providing an in-depth quality analysis for each image captured.
One of the unique features of OptiQual is its ability to confirm the visibility of the veins in the eye, a critical factor for accurate diagnosis. By automatically performing these checks, OptiQual significantly reduces the examination time, making the process more efficient and saving up to 40% of the time spent by examiners. This feature allows healthcare providers to serve their patients with more speed and precision.
Particulars: Linux (Ubuntu), Python.