Pit Pattern Classification in Colonoscopy using Wavelets - WaveLab

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Bibliography [40] Naoki Saito. Classification of geophysical acoustic waveforms and extraction of geological information using time-frequency atoms. In 1996 Proceedings of the Computing Section of the American Statistical Association, pages 322–327, 1997. [41] Naoki Saito and Ronald R. Coifman. Local discriminant bases and their applications. J. Mathematical Imaging and Vision, 5(4):337–358, 1995. [42] N. Stergiou et al. Reduction of miss rates of colonic adenomas by zoom chromoendoscopy. International Journal of Colorectal Diseases, 2006. To appear. [43] N. G. Theofanous, D. E. Maroulis, D. K. Iakovidis, G. D. Magoulas, and S. A. Karkanis. Tumor recognition in endoscopic video images using artificial neural network architectures. In 26th EUROMICRO Conference, pages 423–429, September 2000. [44] Marta P. Tjoa and Shankar M. Krishman. Feature extraction for the analysis of colon status from the endoscopic images. BioMedical Engineering OnLine, April 2003. Online available at http://www.pubmedcentral.nih.gov/ articlerender.fcgi?artid=155673 (07.10.2004). [45] S.-Y. Tung, C.-S. Wu, and M.-Y. Su. Magnifying colonoscopy in differentiating neoplastic from nonneoplastic colorectal lesions. American Journal of Gastroenterology, 96:2628–2632, 2001. [46] Mladen Victor Wickerhauser. Adapted Wavelet Analysis from Theory to Software. A K Peters, 1994. 102
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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
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3 Texture classification After the
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3 Texture classification and ⃗x i
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3 Texture classification 34
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4 Automated pit pattern classificat
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Page 60 and 61: 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 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
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Pit Pattern Classification in Colonoscopy using Wavelets - WaveLab

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