Pit Pattern Classification in Colonoscopy using Wavelets - WaveLab

5 Results
Pit Pattern Type I II III-L III-S IV V
I 47 21 7 4 17 3
II 45 14 12 5 19 4
III-L 14 5 42 10 24 5
III-S 0 8 17 33 33 8
IV 21 10 15 13 25 15
V 8 12 4 4 24 48
Table 5.20: Result distribution matrix for CCLDB for 6 classes (Pit pattern images)
pit pattern type has a classification rate exceeding 48%. But as already mentioned above,
this method does not suffer from low classification rates for pit pattern types II, III-S and V
compared to the other pit pattern types, as shown in table 5.20.
Using this method with the outex images resulted in good results, at least for the two
classes case, as shown in table 5.4. For the two classes case the best overall result was 85%,
which is the worst result for outex tests compared to all other methods. In the six classes
case the results are even worse. The best result obtained here is only 49% which is a fairly
bad result compared to all previous methods as shown in table 5.5.
Pit Pattern Type Non-Neoplastic Neoplastic
Non-Neoplastic 86 14
Neoplastic 16 84
Table 5.21: Result distribution matrix for CCLDB for 2 classes (Outex images)
Class 1 2 3 4 5 6
1 65 0 13 5 3 13
2 12 47 10 5 22 4
3 19 4 38 6 22 11
4 6 0 8 54 15 17
5 1 11 6 7 58 17
6 3 3 9 8 45 32
Table 5.22: Result distribution matrix for CCLDB for 6 classes (Outex images)
From table 5.21 we see that the classification rates and therefore the misclassification rates
for the two classes in the two classes case are nearly identical. In the six classes case, as
depicted in table 5.22, we see that although the highest classification rates for the classes
are located on the main diagonal, almost any other entry in the table contains a value greater
than 0, which results in a bad classification result for each single class, and thus in a fairly
bad total classification result.
90