Senior Design ProjectMegan Luh

Hao LuoFebrary 17 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) $512MRI $992.00 Hospital $4,909 Anesthesia 718.20 Doctor Charge: $3591

(surgery) total amounts

=10,722.20 

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

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

Flow Chart (Stage1)

Flow Chart (Stage2)

ProgressCircle DetectionLine DetectionContour DetectionCamera Calibration

Next StepLength calculationRatio PerceptionUser Interface

PerformanceAccuracy on Circle

DetectionEffect of Noise90% accurate

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

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.

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.”