Pit Pattern Classification in Colonoscopy using Wavelets - WaveLab

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5 Results classes case all the entries on the main diagonal are very close to 100. From table 5.6 we see that the best test results for the tests with the pit pattern images were obtained using the feature extractors “Subband energy” and “Subband variance”. The feature vector dimensions are s = 9, 9 for the k-NN classifier and s = 50, 15 for the SVM classifier. Additionally the k-value for the k-NN classifier is very similar between the two classes case and the six classes case, with k = 22 and k = 25, respectively. For the test with the outex images, these parameters are different as we can see from table 5.7. The feature vector dimensions as well as the k-values for the k-NN classifier are lower in general. The feature vector dimensions for the classifiers are s = 1, 10 for the k-NN classifier and s = 3, 8 for the SVM classifier. The k-value for the k-NN classifier are very similar between the two classes case and the six classes case, with k = 1 and k = 5, respectively. (a) All classes (b) Non-neoplastic (c) Neoplastic Figure 5.8: Results for the WT method (2 classes, k-NN, Pit pattern images) In figure 5.8 we see the overall classification results for the two classes case for the pit pattern images in conjunction with the k-NN classifier. As we can see the best overall 80
5.2 Results (a) All classes (b) Non-neoplastic (c) Neoplastic Figure 5.9: Results for the WT method (2 classes, SVM, Pit pattern images) classification results are obtained for values for k between 10 and 70. Apart from that we see that for a given value for k the results remain constant for values for s between 10 and 50. The reason for this is the fact that for this test set we used a decomposition level of 3 which results in a total number of 10 subbands after a pyramidal decomposition. Thus the method can only use 10 subbands and therefore higher values for s are clamped to a value of 10, which results in the same classification results. For the separate classes we see a similar behaviour like for the BBCB method in figure 5.5. While for the non-neoplastic images smaller values for k result in better classification results it is the other way round for the neoplastic images. Apart from that we clearly see that the combination of s and k exhibiting the best results for the neoplastic images delivers the worst results for the non-neoplastic images. In figure 5.9 we again see the overall classification results for the two classes case for the pit pattern images, but this time for the SVM classifier. The results are very similar to the results shown above for the k-NN classifier. The results remain constant as soon as s exceeds 13. In this case this value is a bit higher than above for the k-NN classifier since we used a decomposition level of 4 for this test set. Interestingly the results for the non-neoplastic start high and get lower with an increasing value for s, while it is the other way round for the neoplastic images. This is very similar to 81
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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
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 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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