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A simulation study with MR images and phantom cartilage images has been carried out to test and validate the efficiency of the proposed UI and diagnostic system. A 3D volume is obtained by successive stacking of the 2D image slices for wavelet analysis. In this study we have used a 3D undecimated wavelet transform for cartilage detection as shown in Fig. (5.2).

Depending on the requirement of the user or study, the volume is generated for a specified wavelet resolution. It can be changed at any time on input from the user. Each processed volume with wavelet analysis is used to build a 3D model of the cartilage at that given wavelet resolution. The model is obtained by extracting an iso-surface of specific scalar value and the volume rendered into a polygonal mesh. A similar such cartilage model for two wavelet resolutions at scalej= 0 and j= 1 for the phantom cartilage and MRI

a

b

c

d

Figure 5.4: a & b) Comparison of phantom cartilage surface at two wavelet resolu-

tions, c & d) Comparison of cartilage surface obtained from MRI volume using wavelet decomposition at two resolutions

volume is shown in Fig. ( 5.4). In Fig. (5.4) b and Fig. (5.4) d one can observe the high resolution cartilage model which is independent of the resolution of the computer screen and is a relatively enhanced version of the prior wavelet resolution level. If there exists any minor deformity or changes on the cartilage surface this will be enhanced by wavelets and can be observed at higher resolution levels. As result one can use high wavelet resolutions to look for any minor changes or alterations which are not visible at current resolution with the naked eye.

In addition cartilage diagnostic parameters such as smoothness, entropy and volume of the cartilage model at multiple wavelet resolutions are given in Table 5.1, where R indicates resolution level. The computed image parameters for texture and smoothness

Figure 5.5: Software framework displaying cartilage surface parameters with the

command prompt as demonstrated on the left and the original cartilage model on the right

of the cartilage surface along with structural information of the cartilage are used by the proposed software framework for cartilage analysis. Furthermore in Table 5.1. we can see that for phantom cartilage we have relatively higher surface smoothness and uniformity at resolution level 2 as compared to wavelet resolution level 1. Also due to the improved signal to noise ratio at higher resolution we can observe significant reduc- tion in entropy as compared with resolution level 1. In the case of the cartilage model obtained from the MRI image we see a lower smoothness and uniformity at higher reso- lution as compared to lower, this is primarily due to deformity on the cartilage surface which is enhanced at higher resolution as a result of which smoothness is reduced de- spite improvement in cartilage resolution at higher wavelet scale. Similar changes are also observed in entropy values. Thus cartilage information at higher resolution along with image parameters at current scale together provide more reliable information of the cartilage structure that can aid in early stage diagnosis for OA. Smoothness in this study is normalized between 0 to 1. The higher the smoothness the lower the value and closer it is to zero. Hence, the phantom cartilage displays more smoothness at higher resolution. The MRI cartilage is degraded which contributes to the signal and at higher resolution indicates a more coarser structure as compared to phantom tissue. Uniformity on the other hand is normalized between 0 to 1, but a higher value closer to 1 indicates a more uniform structure as against a lower value. As a result, at higher resolu- tion the phantom cartilage demonstrates more uniformity as compared to MRI cartilage.

Figure 5.6: Software framework a) displaying 3D cartilage surface parameters, b)

display of original 3D volume and c) display of sub-volume or local region of cartilage when a location is picked on the cartilage surface shown in b using the mouse pointer

Fig. (5.5) demonstrates the basic functionality of the software framework in displaying cartilage surface and computation of its surface parameters such as smoothness, entropy etc. The proposed framework also has an option to offer simultaneous visualization of 3D cartilage edge or model and its sub-volume. In Fig. (5.6) c we have displayed car- tilage sub-volume obtained by selecting position on the cartilage model given in Fig. (5.6) b with help of user input via a computer mouse. As proposed, the framework enables cartilage analysis by selecting or zooming in on the cartilage surface as shown in Fig. (5.6). In addition the proposed framework enables visualization of the cartilage as a grey-scale volume as demonstrated in Fig. (5.7). Currently the multiresolution widget can automatically compute the cartilage parameters without any input by the user which can be used by the proposed grading system. This widget feature can be customized by the user depending on the requirement of their study. At the moment extension of this application is left for future work.

Fig. (5.8) demonstrates a few more UI features where the user can compute the dis- tance between cartilage endpoints and visualize prominent edges on the cartilage surface with help of a glyph. In addition Fig. ( 5.9) a displays MRI dataset indicating early- intermediate OA with early changes on cartilage surface while Fig. ( 5.9) b displays a

Figure 5.7: Software framework displaying MRI volume

Table 5.1: Image Parameters for cartilage computed at two wavelet resolutions

Image Parameters Phantom cartilage MRI cartilage

R1 R2 R1 R2

Volume 39274.7 43531.9 225866 227158

Smoothness 8.799e-8 6.0461e-8 8.3777e-7 9.4497e-7

Uniformity 0.97648 0.97794 0.0196 0.0097

Entropy -0.1519 -0.1371 -6.7576 -7.3583

dataset demonstrating late OA.