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Senior Design ProjectMegan Luh
Hao LuoFebrary 17 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) $512MRI $992.00 Hospital $4,909 Anesthesia 718.20 Doctor Charge: $3591
(surgery) total amounts
=10,722.20
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Interview with Dr. ChristieFounder of the Vanderbilt Arthritis and Joint
Replacement Center. Co-founder of the Southern Joint
Replacement InstituteTopics:
Surgical spatial constraintsInitial incision = 6 inchesInitial tibia leveling = approximately 10 mm
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MarkerDesigning 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
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Flow Chart (Stage1)
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Flow Chart (Stage2)
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ProgressCircle DetectionLine DetectionContour DetectionCamera Calibration
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Next StepLength calculationRatio PerceptionUser Interface
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PerformanceAccuracy on Circle
DetectionEffect of Noise90% accurate
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Testing StrategyNeed an experimental procedure to quantify
the success of our programWant 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 modelTibia: modeled with a cylindrical PVC pipeTest camera at different distances and different
angles
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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.
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.
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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).
Levent Kosumdok. “Plastic with special built-in function.”