Pit Pattern Classification in Colonoscopy using Wavelets - WaveLab

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5 Results (a) Canvas002 (b) Carpet002 (c) Canvas011 (d) Canvas032 (e) Carpet009 (f) Tile006 Figure 5.2: Example images taken from the Outex image database. If we want to use a combination of the green and blue color component for example 64
5.2 Results for the decomposition process, the intensity would be calculated by the equation ( i = min ( 0, min(g, b) ) + max(0, max(g, b) )) /2 = max(g, b)/2 (5.2) This parameter affected tests only which are based on the endoscopic images, since the Outex images do not contain any color information. • a cost function for best basis based methods (see section 2.6.1.1). • a quadtree distance metric (see section 4.3). • the number of subbands to use for feature extraction (1-50). • the number of neighbours for the k-NN classifier (1-100) (see section 3.3.1). • the type of feature to extract from a given subband (see section 4.2.1). • the type of discriminant measure for the LDB algorithm (see section 2.6.1.3). • the SVM gridsearch parameters ν and γ (see section 3.3.3). Looking at this list of parameters it is obvious that testing our algorithms with the different choices resulted in a huge number of different tests. 5.2 Results In this section we will present the best results from our experiments for each of our implemented methods. Since, as mentioned above, the number of test performed during our experiments, we select the feature extraction and classification configuration based on the maximal value of correctly classified images, applied to the 2 or 6 classes case. Tables 5.3, 5.4 and 5.5 show the final results of our experiments. The values throughout these tables are the number of true positives divided by the overall number of images in the according class. It is also important to mention that the results are rounded throughout all tables presented in this chapter. In the following sections we well investigate the results in more detail. 5.2.1 Best-basis method (BB) The best results with this method have been obtained using the gray scale versions of the images (i.e. the color information contained within the original pit pattern images has been discarded). As we can see in table 5.3, in the two classes case the best result achieved was 70% using the SVM classifier. The best result achieved with the k-NN classifier was clearly lower with 65
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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
Page 22 and 23: 2 Wavelets 2.5 Pyramidal wavelet tr
Page 24 and 25: 2 Wavelets Logarithm of energy (Log
Page 26 and 27: 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 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 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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