Pit Pattern Classification in Colonoscopy using Wavelets - WaveLab

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5 Results (a) All classes (b) Class 1 (c) Class 2 (d) Class 3 (e) Class 4 (f) Class 5 (g) Class 6 Figure 5.7: Results for the BBCB method (6 classes, SVM, Outex images) 78
5.2 Results Pit Pattern Type Non-Neoplastic Neoplastic k-NN Non-Neoplastic 54 45 Neoplastic 25 74 SVM Non-Neoplastic 57 42 Neoplastic 21 78 Table 5.12: Result distribution matrices for WT for 2 classes (Pit pattern images) Pit Pattern Type I II III-L III-S IV V k-NN I 61 9 8 0 21 0 II 14 36 14 0 35 0 III-L 33 8 33 0 23 0 III-S 25 0 50 0 25 0 IV 19 17 8 0 53 0 V 12 24 8 0 56 0 SVM I 47 19 17 0 16 0 II 17 43 12 1 22 1 III-L 22 8 33 0 33 1 III-S 8 0 41 8 41 0 IV 14 16 17 0 47 3 V 16 16 20 0 36 12 Table 5.13: Result distribution matrices for WT for 6 classes (Pit pattern images) The tables 5.12 and 5.13 show the result distribution matrices for the two classes case and the six classes case, respectively, using pit pattern images. For the two classes case, as already stated above, we observe significantly better classification results for the neoplastic images. For the six classes case we again observe a poor classification performance for pit pattern types III-S and V, just like in the previous two methods. The method has been tested with the outex images too, which again resulted in excellent classification results, as can been seen in the tables 5.4 and 5.5. In the two classes case the total classification result was 100% for the k-NN classifier as well as for the SVM classifier. In the six classes case the classification result was 98% for the k-NN classifier and only slightly below 100% for some separate classes. The result achieved with the SVM classifier is pretty similar with an overall classification result of nearly 100%. The result distribution matrix for the two classes case is a diagonal matrix, containing 100’s only on the main diagonal and zero at all other positions in the matrix. In the six 79
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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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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 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 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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