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Real-Time Knee Alignment Verification System with Visual Recognition Technology

This senior design project aims to develop a cost-efficient limb alignment verification system for surgical procedures. By utilizing visual recognition software and imaging technology, the project seeks to enhance surgical accuracy in real-time, leading to improved outcomes for both patients and surgeons.

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Real-Time Knee Alignment Verification System with Visual Recognition Technology

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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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