A Wireless Real-Time Multiple-Lenses Panoramic Image Tracking System Design for Minimally Invasive Surgery

• Main Authors: Chun-Hsiang Peng, 彭俊翔
• Other Authors: Ching-Hwa Cheng
• Format: Others
• Language: zh-TW
• Published: 2014
• Online Access: http://ndltd.ncl.edu.tw/handle/2w835n

碩士 === 逢甲大學 === 電子工程學系 === 102 === Minimally Invasive Surgery (MIS) is a current trend in surgery. Compared to traditional surgery, MIS can substantially decrease recovery time and expenses needed by patients after surgeries and reduce pain during surgical procedures. It is also highly regarded by physicians and patients. An endoscope is widely used in the diagnosis and treatments of various medical disciplines, such as hysteroscopy, laparoscopy, and colonoscopy, and has been adopted by many branches of medicine. However, the limited image field of MIS is often the most difficult obstacle faced by surgeons and medical students, especially to less experienced physicians, and during difficult surgical procedures. The limited field of view of endoscopic imaging does not provide a whole picture of the surgery area, making the procedures difficult and full of uncertainty. In light of this problem, we propose &;quot;A Wireless Real-Time Multiple-Lenses Panoramic Image Tracking System Design for Minimally Invasive Surgery&;quot;, hoping to provide physicians with a wide field of view for endoscopic image. We combine images captured from two parallel-mounted endoscope lenses into a single, wide-angle image, giving physicians a wider field of view and easier access to the surgical area. We use the Hue, Saturation and Value (HSV) color to lock the lesion and surgical instruments, and to calculate distance and angles between them. In addition, we develop a wireless transmission system so the image can be transmitted to various display platforms, eliminating the need for excessive cabling on surgical tools and enabling physicians to better operate on the patient. Finally, our system allows surgical assistants a better view of the operation process, and enables other physicians and nurses to remotely observe the process. Our experiment results have shown that we can increase the image to 155% of its original size. We use the Pandaboard ES to verify of our algorithms, and plan to continuously implementing animal trials to verify the reliability of our system.