Knee Alignment Verification System Utilizing Visual Recognition Technology and Imaging
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Transcript of Knee Alignment Verification System Utilizing Visual Recognition Technology and Imaging
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Knee Alignment Verification System Utilizing Visual
Recognition Technology and Imaging
Senior Design ProjectMegan Luh
Hao LuoJanuary 21 2010
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AnalysisProblem Statement
Current methods of limb alignment are costly and time consuming
Dependent on individual surgeon skill for accurate calibration
Performance CriteriaConstrained by surgical
space, time, and resources
Limited by lens quality, camera resolution and frame rate, and noise level
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Primary ObjectiveProof 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.
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HypothesisSolution: 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
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FactorsParameters
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 costsDoctor 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
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Flow Chart
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ProgressCircle DetectionLine DetectionContour Detection
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Next StepLength calculationDesign capCamera calibration
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PerformanceAccuracy
Effect of Noise90% accurate
Precision0.01mm to 1mm
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ConclusionThe 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 OpenCV and cameras to detect markers on a cap placed on the tibial head.
we hope to continue expanding the program to incorporate depth perception and to calculate alignment.
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ReferencesDuda, 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).