Pit Pattern Classification in Colonoscopy using Wavelets - WaveLab

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5 Results Pit Pattern Type I II III-L III-S IV V k-NN I 49 0 5 0 45 0 II 36 0 14 0 49 0 III-L 30 0 42 0 27 0 III-S 25 0 50 0 25 0 IV 33 0 14 0 51 0 V 40 0 8 0 52 0 SVM I 56 11 14 5 12 2 II 33 37 2 4 19 5 III-L 27 5 27 8 27 5 III-S 25 0 50 8 8 8 IV 27 13 13 2 40 4 V 36 12 12 0 24 16 Table 5.9: Result distribution matrices for BB for 6 classes (Pit pattern images) tractor “Subband energy”. The feature vector dimensions are quite different among the classifiers used, with s = 1, 1 for the k-NN classifier and s = 8, 41 for the SVM classifier (note that these values for s correspond to the number of subbands used to compute features for the two classes case and the six classes case, respectively). Additionally the k-value for the k-NN classifier differs very much between the two classes case and the six classes case, with k = 14 and k = 50, respectively. Figure 5.3 shows the results obtained for different choices for s and k in the six classes case using pit pattern images and the k-NN classifier. While figure 5.3(a) shows the classification results for all classes, figures 5.3(b)-(g) show the classification results for the separate classes. In these figures the color shows the classification result for a specific combination of s and k, where a black pixel denotes a classification rate of 0% and yellow denotes a classifcation result of 100%. As we can see from 5.3(a) the best overall classification results are obtained by using a small value for s, while the choice of k does not play such an important role. As already pointed out above, we can clearly see that for pit pattern types II, III-S and V the classification accuracy is very low no matter what choice we make for s and k. For all other types we clearly obtain better classification results. 70
5.2 Results (a) All classes (b) Pit Pattern I (c) Pit Pattern II (d) Pit Pattern III-L (e) Pit Pattern III-S (f) Pit Pattern IV (g) Pit Pattern V Figure 5.3: Results for the BB method (6 classes, k-NN, pit pattern images) 71
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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
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 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 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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