Pit Pattern Classification in Colonoscopy using Wavelets - WaveLab

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List of Figures 96
List of Tables 1.1 The characteristics of the different pit pattern types. . . . . . . . . . . . . . 4 4.1 Distances to tree A according to figure 4.2 using “distance by unique nodes” 49 4.2 Distances to tree A according to figure 4.2 using “distance by decomposition strings” . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 5.1 Histopathological classification of the images acquired . . . . . . . . . . . 61 5.2 Details about the Outex images used (6 classes) . . . . . . . . . . . . . . . 63 5.3 Percentage of correctly classified images (Pit pattern images) . . . . . . . . 66 5.4 Percentage of correctly classified images (Outex images, 2 classes) . . . . . 67 5.5 Percentage of correctly classified images (Outex images, 6 classes) . . . . . 67 5.6 Test configuration details for the best results obtained (Pit pattern images) . 68 5.7 Test configuration details for the best results obtained (Outex images) . . . 69 5.8 Result distribution matrices for BB for 2 classes (Pit pattern images) . . . . 69 5.9 Result distribution matrices for BB for 6 classes (Pit pattern images) . . . . 70 5.10 Result distribution matrices for BBCB for 2 classes (Pit pattern images) . . 73 5.11 Result distribution matrices for BBCB for 6 classes (Pit pattern images) . . 75 5.12 Result distribution matrices for WT for 2 classes (Pit pattern images) . . . . 79 5.13 Result distribution matrices for WT for 6 classes (Pit pattern images) . . . . 79 5.14 Result distribution matrices for LDB for 2 classes (Pit pattern images) . . . 82 5.15 Result distribution matrices for LDB for 6 classes (Pit pattern images) . . . 83 5.16 Result distribution matrix for CC for 2 classes (Pit pattern images) . . . . . 87 5.17 Result distribution matrix for CC for 6 classes (Pit pattern images) . . . . . 87 5.18 Result distribution matrix for CC for 6 classes (Outex images) . . . . . . . 88 5.19 Result distribution matrix for CCLDB for 2 classes (Pit pattern images) . . 89 5.20 Result distribution matrix for CCLDB for 6 classes (Pit pattern images) . . 90 5.21 Result distribution matrix for CCLDB for 2 classes (Outex images) . . . . . 90 5.22 Result distribution matrix for CCLDB for 6 classes (Outex images) . . . . . 90 97
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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
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2 Wavelets Now, that we have define
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2 Wavelets 2.5 Pyramidal wavelet tr
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2 Wavelets Logarithm of energy (Log
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2 Wavelets While the best-basis alg
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2 Wavelets Euclidean distance D(p a
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2 Wavelets 22
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3 Texture classification image retr
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3 Texture classification By approxi
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3 Texture classification where E i
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3 Texture classification After the
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3 Texture classification and ⃗x i
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3 Texture classification 34
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4 Automated pit pattern classificat
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Page 54 and 55: 4 Automated pit pattern classificat
Page 70 and 71: 5 Results (a) Pit Pattern I (b) Pit
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 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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