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Figure 7.20 Model in .vvd format imported into RapidForm

Figure 7.21 Final joint model after the bearing surface identfication process

For the MTM, the operating process can be done either manually or automatically. In this method, we first find two crest points and one trough point as control points, and then other control points are sampled uniformly on the basis of the parameter of each point on the feature curves. To find the extreme points, the feature curve is first tessellated, and then each tessellated point is compared with its neighbouring points. With this method, the extreme points (maximum and minimum point) can be identified.

The EM is almost the same as the scheme described in section 7.5.3. We first find the mutual intersection line of all section planes, which are used to construct intersection rays. Then the middle point of the intersection line is calculated and marked as a centre point. We then construct equiangular rays in each equiangular plane to intersect with the bone model. The intersection points are then saved into the database as the control points.

Using these parameterizing methods, control point information of the feature curves can be extracted and saved into the database. These are fed automatically into statistical software for further analysis, which is extensively used in construction of the generalized model.

Figure 7.20 Model in .vvd format imported into RapidForm

7.6 Database Construction and Surface Generalization

As discussed previously, the design for finger joint surface replacement is an effective solution for patients who suffer arthritis disease. One of the critical requirements in the prosthesis is to design artificial joints that are close in surface geometry to the original bone joints so that the best performance of the artificial joints can be obtained.

To achieve this goal, the real geometries of human finger joint bones must be derived first. A reverse engineering based method is employed to extract the surface features of the bones. Since each finger joint bone carries its own unique geometrical features, it is impractical to make custom-made finger joint prostheses from surgical or economical points of view. To resolve this, a novel method based on statistical analysis is proposed to offer a generalized model for finger joint replacement. A finger joint database is constructed for the purpose of data retrieval, and the geometries of the sampled bones are then categorized into several classes according to the real sizes of the finger joints.

7.6.1 Finger joint database construction

In clinical practice, the contact surfaces of the finger joint prove to be critical to the finger joint replacement surgery because the finger joint replacement prosthesis is designed to replace the corresponding damaged bearing surfaces. For this reason, we only focus on the shapes of the finger joints in the head and base section. This principle is applied to all the data samples in the constructed database.

We sampled 79 finger joint bone specimens from ten hands of nine cadavers in total, eight of which are from left-hand joints and the other two (specimens 9 and 6) from right hands. Specimens 6 and 1 are from the same cadaver. For each hand specimen, the MP and PP are extracted from four fingers of each hand excepting the thumb. The head of the PP and the base of the PP and MP were scanned with the Minolta laser scanner.

The following statistical analyses were used to construct the generalized models for the finger joint shapes, based on whole sampled specimens. We analysed the specimens in two directions: first for the statistical dimension property, which is used roughly to characterize the general sizes of each finger joint; then statistical geometry shape analysis is applied to evaluate the surface geometrical features of finger joint samples. Statistical dimension analysis

Several key parameters of the finger joint dimensions are defined in Table 7.1. The dimensions of the finger joint lengths and widths and the radii of the minimum best-fit circles are extracted. Figure 7.22 showss the general parameters extracted from the PP in the sagittal plane.

The bone length distribution for all samples is shown in Figures 7.23 and 7.24. Figure 7.25 illustrates the PP lengths from individual finger bone specimens.

From Figure 7.23, it is found that the finger joint length distribution is somewhat uneven: only one sample at 38 mm, and there are fewer smaller than larger samples. It can be seen, that although this is quite a good sample size for this kind of project, a larger sample may come up with some different results.

Table 7.1 General parameters and their descriptions extracted from the PP and MP



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Arthritis Relief and Prevention

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