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 5.12: Results for the LDB method (6 classes, k-NN, Outex images) 86
5.2 Results As we can see from table 5.3, in the two classes case the best result obtained for the pit pattern images was 74%. It is worthwhile to mention that the classification of the nonneoplastic images results in a very high classification rate of 95% while the classification of the neoplastic images reaches a classification rate of 59% only. In the six classes case the classification result is 43% with a very high classification rate of 95% for pit pattern type I and very high misclassification rates for pit pattern types II, III-S and V. Pit Pattern Type Non-Neoplastic Neoplastic Non-Neoplastic 95 5 Neoplastic 41 59 Table 5.16: Result distribution matrix for CC for 2 classes (Pit pattern images) Pit Pattern Type I II III-L III-S IV V I 95 0 5 0 0 0 II 91 0 9 0 0 0 III-L 20 0 7 0 73 0 III-S 25 0 25 0 50 0 IV 46 0 2 0 52 0 V 72 0 20 0 0 8 Table 5.17: Result distribution matrix for CC for 6 classes (Pit pattern images) The tables 5.16 and 5.17 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 nonneoplastic images. For the six classes case we observe a poor classification performance for pit pattern types II, III-S and V. If wee look at table 5.17, we see that most of the type II images are misclassified as being of type I, which explains, why the classification for type II is that poor. This behaviour can also be observed for images of type V. Figure 5.13 shows the distance matrices for the two classes and the six classes case. The red bars at the top and the left of the images denote the different classes. Compared to the example distance matrices presented in figure 4.5 (section 4.3.4) we can clearly see, that there is no visible structure in these matrices. Testing this method with the outex images resulted in fairly good results as can be seen in tables 5.4 and 5.5. For the two classes case the best overall result was 98%, while in the six classes case the best classification result is only 74% which is a fairly bad result compared to all previous methods. While in the two classes case the result distribution matrix is nearly identical to the result distribution matrices for the previous methods in conjunction with the outex images, the 87
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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
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 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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