| Summary: | 碩士 === 國立成功大學 === 資訊工程學系碩博士班 === 94 === The main purpose of this thesis is to establish a system, which detects bone spurs on the knee joint from two-dimensional X-ray images (AP view), to provide an objective basis for doctors to perform a powerful diagnosis. We also reconstruct the three-dimensional knee joint model from these stacks of two-dimensional MR volume images at three directions to test and verify whether the contours of bone spur we found conform to three-dimensional knee joint model we reconstruct.
In the two-dimensional X-ray images, we divide the knee joint into three parts (femur, knee joint, and tibia) and process them individually. In the part of knee joint, we adopt the strategy to define the initial normal knee joint contour first, and then search the real knee joint contour outward. Next, we use the information of these contours and add the knowledge from pathology to detect bone spurs based on fuzzy logic control system. In the parts of femur and tibia, the border of femur and tibia is detected using canny edge operator and morphological dilation, and then we find the axis of femur and tibia. Finally, the slope of the axis for corresponding three-dimensional MR knee joint model is calculated. In the MR image, we use the slices from three directions to register and interpolate the volume data. We then pick up one direction to segment the volume data. At last, the knee joint model can be constructed by marching cube algorithm and refined by surface inflation.
In order to compare the two-dimensional X-ray image with three-dimensional MR knee joint model, we have to adjust the angle and scale of the knee joint model to match the X-ray image. After alignment, we can observe that the bone spur on the 3D knee joint model and really conforms to the bone spur in the 2D X-ray images. We also design related experiments to calculate the corresponding error between three-dimensional knee joint model and two-dimensional X-ray images.
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