Pit Pattern Classification in Colonoscopy using Wavelets - WaveLab

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5 Results (a) Pit Pattern I (b) Pit Pattern II (c) Pit Pattern III-S (d) Pit Pattern III-L (e) Pit Pattern IV (f) Pit Pattern V Figure 5.1: Example images taken from our test set of endoscopic images. The second set of images, used throughout our experiments, was taken from the Outex image database [1]. This image database provides gray scale images at different resolutions 62
5.1 Test setup exhibiting many classes of patterns for which always the same number of images is available. For our experiments we chose images of size 128 × 128 pixel. Table 5.2 shows the used classes and the number of images used from these classes for the 2-class and 6-class case. Class Canvas002 Carpet002 Canvas011 Canvas032 Carpet009 Tile006 2 classes 180 180 6 classes 180 180 180 180 180 180 Table 5.2: Details about the Outex images used (6 classes) Figure 5.2 shows five different examples images from each class of the used Outex images. One advantage of the Outex images is the fact, that we have the same number of images among all image classes, which is not the case for the endoscopic images as already pointed out above. 5.1.2 Test scenarios The methods introduced in chapter 4 are based on parameters for which the best choices were not known beforehand. Therefore during our tests we had to test several different values for each of the parameters, which are • the wavelet family to be used for the decomposition process (Haar, Daubechies 4, Daubechies 8 or the biorthogonal Daubechies 7/9). • the maximum depth of wavelet decomposition (3-5). • a specific quadrant for further wavelet decomposition. This parameter limits the decomposition to one specific quadrant (child node in terms of the decomposition tree), but - if set to a specific quadrant - this parameter is only used for the first decomposition step. All further decomposition steps are done for all four possible sub-quadrants of the chosen quadrant. • a specific color channel combination to be used for the wavelet decomposition process (Intensity, Red, Green, Blue or a combination of Red, Green and Blue). The intensity i is calculated by the simple equation ( i = min ( r, min(g, b) ) + max(r, max(g, b) )) /2 (5.1) where r, g and b are the color components representing red, green and blue, respectively. For combinations of these color channels we simply set the components to be discarded to zero in this formula. 63
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Pit Pattern Classification in Colon
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Preface Time is the school in which
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Contents 4.3.1 A quick quadtree int
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1 Introduction endoscope represents
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1 Introduction Pit pattern type I I
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1 Introduction 6
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2 Wavelets (a) Haar (b) Mexican hat
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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
Page 72 and 73: 5 Results (a) Canvas002 (b) Carpet0
Page 74 and 75: 5 Results Pit Pattern Type I II III
Page 76 and 77: 5 Results Feature Feature vector le
Page 80 and 81: 5 Results 5.2.2 Best-basis method b
Page 82 and 83: 5 Results (a) All classes (b) Pit P
Page 84 and 85: 5 Results (a) All classes (b) Non-n
Page 86 and 87: 5 Results (a) All classes (b) Class
Page 88 and 89: 5 Results classes case all the entr
Page 90 and 91: 5 Results the behaviour described a
Page 92 and 93: 5 Results (a) All classes (b) Non-n
Page 94 and 95: 5 Results (a) All classes (b) Class
Page 96 and 97: 5 Results (a) 2 classes (b) 6 class
Page 100 and 101: 5 Results and 85%, respectively. Th
Page 102 and 103: 6 Conclusion correctly. But also in
Page 104 and 105: List of Figures 96
Page 106 and 107: List of Tables 98
Page 108 and 109: Bibliography [13] Huang C.R., Sheu
Page 110: Bibliography [40] Naoki Saito. Clas
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Pit Pattern Classification in Colonoscopy using Wavelets - WaveLab

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