Pit Pattern Classification in Colonoscopy using Wavelets - WaveLab

5.2 Results
As we can see from table 5.3, in the two classes case the best result obtained for the pit
pattern images was 74%. It is worthwhile to mention that the classification of the nonneoplastic
images results in a very high classification rate of 95% while the classification of
the neoplastic images reaches a classification rate of 59% only.
In the six classes case the classification result is 43% with a very high classification rate
of 95% for pit pattern type I and very high misclassification rates for pit pattern types II,
III-S and V.
Pit Pattern Type Non-Neoplastic Neoplastic
Non-Neoplastic 95 5
Neoplastic 41 59
Table 5.16: Result distribution matrix for CC for 2 classes (Pit pattern images)
Pit Pattern Type I II III-L III-S IV V
I 95 0 5 0 0 0
II 91 0 9 0 0 0
III-L 20 0 7 0 73 0
III-S 25 0 25 0 50 0
IV 46 0 2 0 52 0
V 72 0 20 0 0 8
Table 5.17: Result distribution matrix for CC for 6 classes (Pit pattern images)
The tables 5.16 and 5.17 show the result distribution matrices for the two classes case
and the six classes case, respectively, using pit pattern images. For the two classes case,
as already stated above, we observe significantly better classification results for the nonneoplastic
images. For the six classes case we observe a poor classification performance for
pit pattern types II, III-S and V. If wee look at table 5.17, we see that most of the type II
images are misclassified as being of type I, which explains, why the classification for type
II is that poor. This behaviour can also be observed for images of type V.
Figure 5.13 shows the distance matrices for the two classes and the six classes case. The
red bars at the top and the left of the images denote the different classes. Compared to the
example distance matrices presented in figure 4.5 (section 4.3.4) we can clearly see, that
there is no visible structure in these matrices.
Testing this method with the outex images resulted in fairly good results as can be seen in
tables 5.4 and 5.5. For the two classes case the best overall result was 98%, while in the six
classes case the best classification result is only 74% which is a fairly bad result compared
to all previous methods.
While in the two classes case the result distribution matrix is nearly identical to the result
distribution matrices for the previous methods in conjunction with the outex images, the
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