Knee Alignment Verification System Utilizing Visual Recognition Technology and Imaging

1. Knee Alignment Verification System Utilizing Visual Recognition Technology and Imaging Senior Design Project Megan Luh Hao Luo Febrary 17 2010

2. Analysis • Problem Statement • Current methods of limb alignment are costly and time consuming • Dependent on individual surgeon skill for accurate calibration • Performance Criteria • Constrained by surgical space, time, and resources • Limited by lens quality, camera resolution and frame rate, and noise level

3. Primary Objective • Proof of Concept that visual recognition software can be applied to the field of limb alignment in real-time for surgical procedures • Improve the method of limb alignment used during surgical procedures • Create a new method that is more efficient, can be used in real-time, more economically profitable for hospitals.

4. Hypothesis • Solution: Utilize computer vision software in real time and implement it for limb alignment • Goals: Create a computer vision system using OpenCV and design necessary components for surgery

5. Factors • Parameters • Quality is determined by the speed, accuracy, and precision of the computer algorithm • Overall operating costs are reduced with a faster system • Patient and surgeon both benefit from a faster, more accurate system • Average operating room costs = $1000.00 per min • Surgical costs • Doctor visits; pre surgery and exams (total 3) $512 • MRI $992.00 • Hospital $4,909 • Anesthesia 718.20 • Doctor Charge: $3591 (surgery) • total amounts =10,722.20 

6. Interview with Dr. Christie • Founder of the Vanderbilt Arthritis and Joint Replacement Center. • Co-founder of the Southern Joint Replacement Institute • Topics: • Surgical spatial constraints • Initial incision = 6 inches • Initial tibia leveling = approximately 10 mm

7. Marker • Designing a cross shape marker with some spheres on it to mark the x-ray • It consists of four spheres connected in a cross configuration • The two pairs of spheres vary in size and in color • Use a biocompatible, disposable plastic with an x-ray contrast medium: polyethylene, polycarbonate

8. Flow Chart (Stage1)

9. Flow Chart (Stage2)

10. Progress • Circle Detection • Line Detection • Contour Detection • Camera Calibration

11. Next Step • Length calculation • Ratio Perception • User Interface

12. Performance • Accuracy on Circle Detection • Effect of Noise • 90% accurate

13. Testing Strategy • Need an experimental procedure to quantify the success of our program • Want to calculate how accurately the camera detects the location of the spheres in 3D space and their spatial orientation • Do this with a simplified experimental model • Tibia: modeled with a cylindrical PVC pipe • Test camera at different distances and different angles

14. Conclusion • The goal of this project is to accomplish a proof of concept that visual recognition software can be applied to the field of orthopedic limb alignment in a real-time surgical procedure. • So far, we have solidified the goal and mapped out the details of software implementation. • Futures works include creating the software, troubleshooting, and testing the result.

15. References • Duda, R. O. and P. E. Hart, "Use of the Hough Transformation to Detect Lines and Curves in Pictures," Comm. ACM, Vol. 15, pp. 11–15 (January, 1972). • Bradski, Gary, and Adrian Kaehler. "Image Transforms, Contours, Project and 3D vision." In Learning OpenCV: Computer Vision with the OpenCV Library. 1st ed. Sebastopol: O'Reilly Media, Inc., 2008. 109-141, 144-190, 222-251, 370-458. • Chleborad, Aaron. "OpenCV's cvReprojectImageTo3D." Graduate Student Robotics Blog. http://people.cis.ksu.edu/~aaron123/?m=20090629 (accessed December 18, 2009). • Levent Kosumdok. “Plastic with special built-in function.”