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Knee Alignment Verification System Utilizing Visual Recognition Technology and Imaging

Knee Alignment Verification System Utilizing Visual Recognition Technology and Imaging. Senior Design Project Megan Luh Hao Luo January 21 2010. Analysis. Problem Statement Current methods of limb alignment are costly and time consuming

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Knee Alignment Verification System Utilizing Visual Recognition Technology and Imaging

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  1. Knee Alignment Verification System Utilizing Visual Recognition Technology and Imaging Senior Design Project Megan Luh HaoLuo January 21 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. Flow Chart

  7. Progress • Circle Detection • Line Detection • Contour Detection

  8. Next Step • Length calculation • Design cap • Camera calibration

  9. Performance • Accuracy • Effect of Noise • 90% accurate • Precision • 0.01mm to 1mm

  10. 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. • We plan to accomplish this by using OpenCVand cameras to detect markers on a cap placed on the tibialhead. • we hope to continue expanding the program to incorporate depth perception and to calculate alignment.

  11. 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).

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