Video-endoscopy (Figure 1), a mode of minimally invasive surgery, has proven to be significantly less invasive to the patient. However, it creates a much more complex operation environment that requires the surgeon to operate through a video interface. Visual feedback control and image interpretation can be difficult. Poor visual feedback in video-endoscopy prolongs the operation time, increases the risk to the patient, and drives up the cost of health care. It is a major roadblock in replacing the traditional, highly traumatic open surgical procedures with the much less invasive, more patient friendly video-endoscopy, and in training the surgeons to master this new mode of operation. Our research objective is thus to design, code, and validate on real images novel image analysis and rectification algorithms to enhance the visual feedback to the surgeon in video-endoscopy.