Pit Pattern Classification in Colonoscopy using Wavelets - WaveLab

1.3 Computer based pit pattern classification
1.3 Computer based pit pattern classification
The motivation behind computer based pit pattern classification is to assist the physician
in analyzing the colon images taken with a colonoscope just in time. Thus a classification
can already be done during the colonoscopy and therefore this makes a fast classification
possible. But as already mentioned above, a final histological finding is needed here too to
confirm the classification made by the computer.
The process of the computer based pit pattern classification can be divided into the following
steps:
1. First of all as many images as possible have to be acquired. These images serve for
training as well as for testing a trained classification algorithm.
2. The images are analyzed for some specific features such as textural features, color
features, frequency domain features or any other type of features.
3. A classification algorithm of choice is trained with the features gathered in the last
step.
4. The classification algorithm is presented some unknown image to classify. The unknown
image in this context is an image which has not been used during the training
step.
From these steps the very important question which features to extract from the images
arises. Since there are many possibilities for image features, chapter 3 will give a short
overview of some possible features for texture classification.
However, as the title of this thesis already suggests, the features we intend to use are solely
based on the wavelet transform. Hence, before we start thinking about possible features
to extract from endoscopic images, the next chapter tries to give a short introduction to
wavelets and the wavelet transform.
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