Pit Pattern Classification in Colonoscopy using Wavelets - WaveLab

3 Texture classification
3.1 Introduction
It is not enough to do your best; you must
know what to do, and then do your best.
- W. Edwards Deming
If a computer program has to discriminate between different classes of images some sort of
classification algorithm has to be applied to the training data during the training phase. During
the classification of some unknown image the formerly trained classification algorithm
is presented the new, unknown image and tries to classify it correctly.
Thus the classification process mainly consist of two parts: the extraction of relevant features
from images and the classification based on these features.
This chapter will give a literature review, presenting approaches which mainly employ
features based on wavelets presented in chapter 2. But we will also see some examples of
endoscopic classification which do not use any wavelet based features.
Apart from the feature extraction this chapter gives an introduction to some well-known
classification algorithms.
3.2 Feature extraction
3.2.1 Wavelet based features
To use wavelet based features, the image to be analyzed has to be decomposed using a
wavelet decomposition method such as the pyramidal wavelet transform or wavelet packets
(see chapter 2). Then, having the resulting subbands, different types of features can be
extracted from the coefficients contained within the according subbands. These include
the mean and standard deviation of coefficients in a subband, features based on wavelet
coefficient histograms, features based on wavelet coefficient co-occurrence matrices (see
section 4.2.1.1) and modeling parameters for wavelet coefficient histograms, just to mention
a few.
The method presented in [5], for example, uses features based on the mean and standard
deviation of coefficients inside of wavelet subbands. Bhagavathy presents a wavelet-based
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