Pit Pattern Classification in Colonoscopy using Wavelets - WaveLab

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4 Automated pit pattern classification feature extraction. This is important since during the feature extraction process the question arises, which subbands to choose to build the feature vector from. If the number of subbands for an image I is denoted by s I the maximum number of subbands which can be used to extract features s max from each of the images is s max = min 1
4.2 Classification based on features with F i = {f 1 , f 2 , . . . , f smax } (4.7) where F is the feature extractor chosen and f i is the feature for a subband S i . But the feature vectors just created can not yet be fed into any classifier, since we still have a major problem. Due to the fact that the best-basis algorithm most times will produce different decomposition structures and therefore different lists of subbands for different images, it can not be assured that later, during the classification process, the features of the same subbands among the images are compared. It is even possible, that a feature for a subband, which is present in the subband list for one image is not present in the subband list for another image. Therefore, when the feature vectors for all images have been created we have to perform the following steps to post-process the feature vectors: 1. We create a dominance tree T D , which is a full wavelet packet tree with a decomposition depth l max (the maximum decomposition depth among all decomposition trees for all images). 2. This dominance tree is now updated using the decomposition tree T i for a training image indexed by i. This updating process is done by comparing the tree T i and T d node by node. For each node T d,j present in T d we try to find the according node T i,j at the same position j in the tree T i . Such a node in the dominance tree holds the following two values: • A counter c d,j , which is incremented by one if the tree T i has a node at the tree position j too, which additionally is marked as being a feature node. Thus, after the dominance tree has been updated with all trees for all training images, this counter stores the number of times that specific node at tree position j was present and selected as a feature node among all trees of the training images. This counter is used later as a measure of relevance for a specific subband and to decide whether that specific subband should be included in the process of feature vector creation. In other words, the higher the counter for a specific node (subband) is, the higher is the chance that the according subband will be chosen to construct a part of the feature vector from its coefficients. • A flag f d,j , indicating whether at least one of the trees of the training images has a node located at tree position j. This flag is set the first time a tree T i has a node located at position j. This flag is used during the pruning process following in the next step. 3. When T D has been updated for all T i , T D gets pruned. This is done by traversing the tree from the leafs upwards to the root and deleting any child nodes not present 37
Page 1: Pit Pattern Classification in Colon
Page 5: Preface Time is the school in which
Page 8 and 9: Contents 4.3.1 A quick quadtree int
Page 10 and 11: 1 Introduction endoscope represents
Page 12 and 13: 1 Introduction Pit pattern type I I
Page 14 and 15: 1 Introduction 6
Page 16 and 17: 2 Wavelets (a) Haar (b) Mexican hat
Page 18 and 19: 2 Wavelets For the discrete wavelet
Page 20 and 21: 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 46 and 47: 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 78 and 79: 5 Results Pit Pattern Type I II III
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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