Supervised Scene Illumination Control in Stereo Arthroscopes for Robot Assisted Minimally Invasive Surgery

Minimally invasive surgery (MIS) offers many advantages to patients but it also imposes limitations on surgeons ability, as no tactile or haptic feedback is available. From medical robotics perspective, visualizations issues specific to MIS such as limited field of view and the lack of automatic exposure control of the surgical area make it challenging when it comes to tracking tissue, tools and camera pose as well as in perceiving depth. Lighting plays an important role in 3D reconstruction and variations due to internal illumination conditions are known to degrade vital visual information. In this work, we describe a supervised adaptive light control system to solve some of the image visualization problems of MIS. Our proposed method is able to classify underexposed and over-exposed frames and adjust lighting condition automatically to enrich image quality. Our method uses support vector machines to classify different illumination conditions. Visual feedback is provided by gradient information to assess image quality and justify classifier decision. The output of this system has been tested against two cadaver knee experiment data with an overall accuracy of 97.75% for under-exposed and 89.11% for over-exposed classes. Hardware implementation of this classifier is expected to result in adaptive lighting for robot assisted surgery as well as in providing support to surgeons by freeing them from manual adjustments to lighting controls.