Pit Pattern Classification in Colonoscopy using Wavelets - WaveLab

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1 Introduction endoscope represents a significant advance in colonoscopic diagnosis as it provides images which are up to 150-fold magnified. This magnification is possible through an individually adjustable lens. Images taken with this type of endoscope are very detailed as they uncover the fine surface structure of the mucosa as well as small lesions. To visually enhance the structure of the mucosa and therefore the structure of an eventual tumorous lesion, a common procedure is to spray indigo carmine or methylen blue onto the mucosa. While dyeing with indigo carmine causes a plastic appearance of the mucosa, dyeing with methylen blue helps to highlight the boundary of a lesion. Cresyl violet, which actually stains the margins of the pit structures, is also often sprayed over a lesion. This method is also referred to as staining. 1.2 Pit patterns Diagnosis of tumorous lesions by endoscopy is always based on some sort of staging, which is a method used to evaluate the progress of cancer in a patient and to see to what extent a tumorous lesion has spread to other parts of the body. Staging is also very important for a physician to choose the right treatment of the colorectal cancer according to the respective stage. Several classification methods have been developed in the past such as Duke’s classification system, the modified Duke staging system and, more recently, the TNM staging system (Tumor, node, metastasis). Another classification system, based on so-called pit patterns of the colonic mucosa, was originally reported by Kudo et al. [16, 30]. As illustrated in figure 1.1 this classification differentiates between five main types according to the mucosal surface of the colon. The higher the type number the higher is the risk of a lesion to be malignant. While lesions of type I and II are benign, representing the normal mucosa or hyperplastic tissue, and in fact are nontumorous, lesions of type III to V in contrast represent lesions which are malignant. Lesions of type I and II can be grouped into non-neoplastic lesions, while lesions of type III to V can be grouped into neoplastic lesions. Thus a coarser grouping of lesions into two instead of six classes is also possible. There exist several studies which found a good correlation between the mucosal pit pattern and the histological findings, where especially techniques using magnifying colonoscopes led to excellent results [23, 27, 28, 29, 42, 17]. As depicted in figure 1.1 pit pattern types I to IV can be characterized fairly well, while type V is a composition of unstructured pits. Table 1.1 contains a short overview of the main characteristics of the different pit pattern types. Figure 1.2 again shows the different pit pattern types, but this time in the third dimension. This makes it easier to understand how the different pit pattern types develop over time. 2
1.2 Pit patterns (a) Pit pattern I (b) Pit pattern II (c) Pit pattern IIIS (d) Pit pattern IIIL (e) Pit pattern IV (f) Pit pattern V Figure 1.1: Pit pattern classification according to Kudo et al. (a) Pit pattern I (b) Pit pattern II (c) Pit pattern IIIS (d) Pit pattern IIIL (e) Pit pattern IV (f) Pit pattern V Figure 1.2: Images showing 3D views of the different types of pit pattern according to Kudo. Although at a first glance this classification scheme seems to be straightforward and easy to be applied, it needs some experience and exercising to achieve fairly good results [22, 45]. 3
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 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 44 and 45: 4 Automated pit pattern classificat
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4 Automated pit pattern classificat
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5 Results (a) Pit Pattern I (b) Pit
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5 Results (a) Canvas002 (b) Carpet0
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5 Results Pit Pattern Type I II III
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5 Results Feature Feature vector le
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5 Results 5.2.2 Best-basis method b
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5 Results (a) All classes (b) Pit P
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5 Results (a) All classes (b) Non-n
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5 Results (a) All classes (b) Class
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5 Results classes case all the entr
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5 Results the behaviour described a
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5 Results (a) All classes (b) Non-n
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5 Results (a) All classes (b) Class
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5 Results (a) 2 classes (b) 6 class
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5 Results and 85%, respectively. Th
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6 Conclusion correctly. But also in
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List of Figures 96
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List of Tables 98
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Bibliography [13] Huang C.R., Sheu
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Bibliography [40] Naoki Saito. Clas
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