A Customized Camera Imaging Pipeline for Dermatological Imaging

Hakki Can Karaimer, Iman Khodadad, Farnoud Kazemzadeh, Michael S. Brown; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2019, pp. 0-0

This paper describes the customization of the camera processing pipeline of a machine vision camera that has been integrated into a hand-held dermatological imaging device. The device uses a combination of visible and non-visible spectral LEDs to allow capture of visible RGB imagery as well as selected non-visible wavelengths. Our customization involves two components. The first component is a color calibration procedure that ensures the captured images are colorimetrically more accurate than those obtained through the machine vision camera's native API. The need for color calibration is a critical component that is often overlooked or poorly understood by computer vision engineers. Our second component is a fast method to integrate the narrow band spectral images (some of which are outside the visible range) into the visible RGB image for enhanced visualization. This component of our pipeline involves evaluating several algorithms capable of multiple image fusion to determine the most suitable one for our application. Quantitative and subject results, including feedback from clinicians, demonstrate the effectiveness of our customization procedure.