Pit Pattern Classification in Colonoscopy using Wavelets - WaveLab

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5 Results (a) All classes (b) Non-neoplastic (c) Neoplastic Figure 5.5: Results for the BBCB method (2 classes, k-NN, pit pattern images) two separate classes shown in 5.5(b) and 5.5(c). While the classification results for the nonneoplastic images seem to get better when using lower values for k, the neoplastic images seems to get better classified the higher we set value k. Regarding the two classes case the overall classification results are pretty constant, not matter what value we choose for s, as we can see from figure 5.6. The classification results for the non-neoplastic images are a bit jagged but the results are always between 60% and 80%. Despite some small spikes, the curve for the neoplastic images is more smooth than the curve for the non-neoplastic images. In figure 5.7 we see the classification results for six classes case using the Outex images in conjunction with the k-NN classifier. As we can see the overall classification results are clearly better than the overall classification results for the pit pattern images. This applies to the results obtained for the the single classes too. For the overall classification result as well as for the classes 3 and 4 it seems like smaller values for k and values for s between 10 and 35 lead to an excellent classification performance, while for the classes 2 and 6 we also obtain good classification results for a higher value of k. For classes 1 and 5 it seems like the value of k has no great effect on the classification results as long as the value of s is chosen between 5 and 35. 76
5.2 Results (a) All classes (b) Non-neoplastic (c) Neoplastic Figure 5.6: Results for the BBCB method (2 classes, SVM, pit pattern images) 5.2.4 Pyramidal decomposition (WT) In contrast to the previous two methods, regarding the tests with the pit pattern images, this method achieved the best classification result using only the R-channel of the pit pattern images (i.e. the information stored in the color channels for green and blue has been discarded). As we can see in table 5.3, in the two classes case the best result obtained for the pit pattern images was 70% using the SVM classifier. The best result achieved with the k-NN classifier was a bit lower, with a percentage of correctly classified images of 62%. Therefore, with this method, the SVM classifier again outperforms the k-NN classifier. However, it is interesting that in contrast to the previous two methods the neoplastic images seem to get significantly more accurately classified than the non-neoplastic ones. This property might be interesting for future extensions, since by combining this method with another method which classifies the non-neoplastic images more accurately, we could possibly expect better overall classification results. In the six classes case again the SVM classifier outperforms the k-NN classifier with a classification result of 56% compared to 45% and the misclassification rates for pit pattern types III-S and V again are very high, just like in the previous method. 77
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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
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 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 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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