Knee Alignment Verification System Utilizing Visual

Recognition Technology and Imaging

Senior Design ProjectMegan Luh

Hao LuoJanuary 21 2010

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

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.

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

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 

Flow Chart

ProgressCircle DetectionLine DetectionContour Detection

Next StepLength calculationDesign capCamera calibration

PerformanceAccuracy

Effect of Noise90% accurate

Precision0.01mm to 1mm

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.

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).