Pit Pattern Classification in Colonoscopy using Wavelets - WaveLab

5 Results
5.2.2 Best-basis method based on structural features (BBS)
As we saw in section 4.3.3.3 this method is similar to the method in the previous section. But
for the tests with this method we used the structural feature extractors FEUNV (based on the
unique node values) and FETS (based on the complete tree structure) already presented in
section 4.3.3.3. Apart from that we used KNNTD (distances based on trees and a quadtree
distance metric instead of feature vectors and a vector distance metric) in conjunction with
the k-NN classifier.
Just like in the previous method, the best results have been obtained using the gray scale
versions of the pit pattern images.
As we can see in table 5.3, in the two classes case the best result obtained for the pit
pattern images was 78% using the SVM classifier. The best result achieved with the k-NN
classifier was clearly lower with a percentage of correctly classified images of 58%.
In the six classes case again the SVM classifier clearly outperforms the k-NN classifier
with a classification result of 56% compared to 35% and the misclassification rates for pit
pattern types III-S and V again are extremely high, just like in the previous method.
Additionally we can see in table 5.3 that the classification performance seems to be superior
for the second class in the two classes case for the k-NN classifier. In the six classes
case the results are similar - while pit pattern type I gets classified clearly worse than type
IV, the classification fails completely for all other types.
Compared to the tests with the pit pattern images, the tests performed using the Outex
images resulted in very high classification results, as can been seen in the tables 5.4 and
5.5. In the two classes case the total classification result was 97% for the k-NN classifier
and 89% for the SVM classifier. In the six classes case the classification result was 53% for
the k-NN classifier. The top result achieved with the SVM classifier is a bit lower with an
overall classification result of 45%.
At this point it is worthwhile to mention that during the first test runs the SVM results
for the Outex images were conspicuously worse with a top result of 0%. After limiting the
number of SVM training samples however, we were able to get clearly higher results. To
achieve these results we trained the SVM classsifier with only 10% out of all images.
From table 5.6 we see, that regarding the pit pattern images in conjunction with the k-NN
classifier the structural feature extractor KNNTD always delivered the best results. While
in the two classes case the best result has been achieved with a value for k of 50, the best
results in the six classes case has been obtained using a smaller k-value of 43. When using
the SVM classifier however, the best results have been achieved using the FEUNV feature
extractor.
For the Outex images in connection with the k-NN classifier the values for k leading to
the best overall classification results are clearly much lower than for the pit pattern images
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