Pit Pattern Classification in Colonoscopy using Wavelets - WaveLab

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5 Results Pit Pattern Type I II III-S III-L IV V Total BEST-BASIS METHOD (BB) k-NN (2 classes) 65 57 60 k-NN (6 classes) 50 0 0 42 52 0 36 SVM (2 classes) 76 66 70 SVM (6 classes) 56 37 8 27 40 16 39 BEST-BASIS METHOD (STRUCTURAL FEATURES) (BBS) k-NN (2 classes) 19 87 58 k-NN (6 classes) 28 0 0 0 89 0 35 SVM (2 classes) 78 77 78 SVM (6 classes) 88 9 0 14 91 8 56 BEST-BASIS CENTROIDS (BBCB) k-NN (2 classes) 66 71 69 k-NN (6 classes) 68 26 0 44 49 0 45 SVM (2 classes) 74 71 72 SVM (6 classes) 52 37 0 44 53 12 56 PYRAMIDAL WAVELET TRANSFORM (WT) k-NN (2 classes) 54 74 62 k-NN (6 classes) 62 37 0 34 54 0 45 SVM (2 classes) 58 78 70 SVM (6 classes) 47 44 8 34 47 12 41 LOCAL DISCRIMINANT BASES (LDB) k-NN (2 classes) 49 88 71 k-NN (6 classes) 61 37 0 58 52 0 45 SVM (2 classes) 67 83 76 SVM (6 classes) 47 47 8 47 59 20 48 CENTROID CLASSIFICATION (CC) 2 classes 95 59 74 6 classes 95 0 0 7 52 3 43 CC BASED ON BB AND LDB (CCLDB) 2 classes 74 67 70 6 classes 47 14 33 42 25 48 34 Table 5.3: Percentage of correctly classified images (Pit pattern images) a percentage of correctly classified images of only 60%. Both, the SVM classifier and the k-NN classifier show slightly different classification results for both classes. The images belonging to the non-neoplastic class are clearly better classified by both classifiers. Regarding the six classes case the best result achieved was 39%, again using the SVM classifier. The best result for the k-NN classifier again is a bit lower with a classification 66
5.2 Results Class 1 2 Total BEST-BASIS METHOD (STRUCTURAL FEATURES) (BBS) k-NN 95 99 97 SVM 72 92 89 BEST-BASIS CENTROIDS (BBCB) k-NN 100 100 100 SVM 100 100 100 PYRAMIDAL WAVELET TRANSFORM (WT) k-NN 100 100 100 SVM 100 100 100 LOCAL DISCRIMIANNT BASES (LDB) k-NN 100 100 100 SVM 100 100 100 CENTROID CLASSIFICATION (CC) 100 96 98 CC BASED ON BB AND LDB (CCLDB) 86 84 85 Table 5.4: Percentage of correctly classified images (Outex images, 2 classes) Class 1 2 3 4 5 6 Total BEST-BASIS METHOD (STRUCTURAL FEATURES) (BBS) k-NN 23 72 57 54 52 58 53 SVM 51 51 41 50 58 18 45 BEST-BASIS CENTROIDS (BBCB) k-NN 99 100 97 94 100 99 98 SVM 100 100 100 99 100 100 100 PYRAMIDAL WAVELET TRANSFORM (WT) k-NN 100 96 100 99 97 93 98 SVM 100 100 99 100 99 99 100 LOCAL DISCRIMINANT BASES (LDB) k-NN 100 100 100 100 100 100 100 SVM 100 100 100 100 99 99 100 CENTROID CLASSIFICATION (CC) 96 93 77 64 52 64 74 CC BASED ON BB AND LDB (CCLDB) 65 47 38 54 58 32 49 Table 5.5: Percentage of correctly classified images (Outex images, 6 classes) 67
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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
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 70 and 71: 5 Results (a) Pit Pattern I (b) Pit
Page 72 and 73: 5 Results (a) Canvas002 (b) Carpet0
Page 76 and 77: 5 Results Feature Feature vector le
Page 78 and 79: 5 Results Pit Pattern Type I II III
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 98 and 99: 5 Results Pit Pattern Type I II III
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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