Pit Pattern Classification in Colonoscopy using Wavelets - WaveLab

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1 Introduction Pit pattern type I II III S III L IV V Characteristics roundish pits which designate a normal mucosa stellar or papillary pits small roundish or tubular pits, which are smaller than the pits of type I roundish or tubular pits, which are larger than the pits of type I branch-like or gyrus-like pits non-structured pits Table 1.1: The characteristics of the different pit pattern types. To show this, figure 1.3 contains images out of the training image set used throughout this thesis. (a) Pit pattern I (b) Pit pattern II (c) Pit pattern IIIS (d) Pit pattern IIIL (e) Pit pattern IV (f) Pit pattern V Figure 1.3: Images showing the different types of pit pattern. From these images it is not as easy anymore to tell which type of pit pattern each of these images represents. Apart from that it is important to mention that a classification solely based on pit pattern classification is not possible. There is always a final histological finding needed for the physician to decide whether a lesion in a colon is tumorous or non-tumorous. 4
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. 5
Page 1: Pit Pattern Classification in Colon
Page 5: Preface Time is the school in which
Page 8 and 9: Contents 4.3.1 A quick quadtree int
Page 10 and 11: 1 Introduction endoscope represents
Page 14 and 15: 1 Introduction 6
Page 16 and 17: 2 Wavelets (a) Haar (b) Mexican hat
Page 18 and 19: 2 Wavelets For the discrete wavelet
Page 20 and 21: 2 Wavelets Now, that we have define
Page 22 and 23: 2 Wavelets 2.5 Pyramidal wavelet tr
Page 24 and 25: 2 Wavelets Logarithm of energy (Log
Page 26 and 27: 2 Wavelets While the best-basis alg
Page 28 and 29: 2 Wavelets Euclidean distance D(p a
Page 30 and 31: 2 Wavelets 22
Page 32 and 33: 3 Texture classification image retr
Page 34 and 35: 3 Texture classification By approxi
Page 36 and 37: 3 Texture classification where E i
Page 38 and 39: 3 Texture classification After the
Page 40 and 41: 3 Texture classification and ⃗x i
Page 42 and 43: 3 Texture classification 34
Page 44 and 45: 4 Automated pit pattern classificat
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4 Automated pit pattern classificat
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5 Results (a) Pit Pattern I (b) Pit
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5 Results (a) Canvas002 (b) Carpet0
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5 Results Pit Pattern Type I II III
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5 Results Feature Feature vector le
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5 Results 5.2.2 Best-basis method b
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5 Results (a) All classes (b) Pit P
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5 Results (a) All classes (b) Non-n
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5 Results (a) All classes (b) Class
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5 Results classes case all the entr
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5 Results the behaviour described a
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5 Results (a) All classes (b) Non-n
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5 Results (a) All classes (b) Class
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5 Results (a) 2 classes (b) 6 class
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5 Results and 85%, respectively. Th
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6 Conclusion correctly. But also in
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List of Figures 96
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List of Tables 98
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Bibliography [13] Huang C.R., Sheu
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Bibliography [40] Naoki Saito. Clas
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Pit Pattern Classification in Colonoscopy using Wavelets - WaveLab

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