Graphical Models and Image Processing
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Topologies for the digital spaces Z2 and Z3
Computer Vision and Image Understanding
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Fuzzy order statistics and their application to fuzzy clustering
IEEE Transactions on Fuzzy Systems
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The main aims of this research are the accuracy check control and ascertaining quality of a new method of knee joint alloarthroplasty. This method has two basic advantages. The first, the prosthesis location precision in the knee joint increases, secondary, the surgical procedure time shortens down to 45−60 min. As a result the infection susceptibility decreases.The first step of the presented method is finding the mechanical axis of the lower limb. In the second step, based on the analysis of each slice of a whole computer tomography series, a radiologist or an orthopedist has to qualify the pathology of bone structures within the knee joint. In this way the section planes within femur and tibia are determined. The third step is based on the three-dimensional model of femur and tibia. In order to extract bone structures from the computer tomography slices of the knee joint, the fuzzy C-means algorithm with median modification has been implemented. These images allow the three-dimensional structure of femur and tibia to be built. In the next step femur and tibia imprints have been created and the 3D models of both bones have been built. On the basis of the computer tomography slices of the knee joint the doctor has to indicate two healthy parts of both bones. Together with the created 3D models they permit building a patient-specific band with two gully holes. The gully holes are dedicated to Kirschner tool location marks. This special-individual band is apposed to selected bone structures of the knee joint during the surgery (knee alloarthroplasty).