Pit Pattern Classification in Colonoscopy using Wavelets - WaveLab

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5 Results and 85%, respectively. The BBS method achieved overall classification results of 97% and 89% for the k-NN classifier and the SVM classifier, respectively. As we can see from table 5.5, the results for the six classes tests return very similar classification rates. Again, the CC method and the CCLDB method return lower classification results of 74% and 49%, respectively, The BBS method delivers 53% and 45% for the k- NN classifier and the SVM classifier, respectively. All other methods return overall results between 97% and 100%. For the outex images we cannot say that a particular classifier performs better, since both classifiers used return only slightly different results, if at all. Only for the BBS method it seems that k-NN classifier outperforms the SVM classifier. The classification rates for the separate classes are all very much alike, in contrast to the test with the pit pattern images. As we already saw above, the feature vector dimensions as well as the k-values are rather low compared to the pit pattern tests. In the two classes cases the feature vectors have dimensions between 1 and 4 only, while in the six classes cases the dimensions are quite higher with values between 7 and 43. The k-values are equally low among all tests with values between 1 and 5. 92
6 Conclusion It is good to have an end to journey toward, but it is the journey that matters in the end. - Ursula K. LeGuin The main aim of this work was to investigate whether computer assisted pit pattern classification is feasible. For this purpose we first shortly introduced the concept of wavelets and some possible classification methods. Based on these preliminaries we then implemented various wavelet based methods such as Local discriminant bases, Pyramidal decomposition, Best-basis based methods, Centroid classification and combinations of these methods. When it came to the choice of a classifier we restricted our tests to k-NN and SVM, since the first one is easy to implement, while the latter one has strong capabilities in adapting to more complex classification problems. To have comparable results we did not limit our tests to the pit pattern images only. We also used the Outex image library to test the classification abilities of our methods in general. From the previous chapter we see that the results already obtained by our methods are encouraging. Especially the classification of pit pattern images in the two classes case delivers promising results for some methods. On the other hand, we also see that especially the classification of neoplastic lesions in the six classes case is a serious problem. Examining the results it is obvious that for a clinical usage there is still a physician needed who still provides significantly higher recognition rates. One possible explanation of the results for the pit pattern images, as already mentioned repeatedly in the previous chapters, is that the image set we were provided for our experiments was rather limited. Especially for the pit pattern types III-S and V we only had very few images, due to the fact that these types of lesions are rare compared to the other types. Thus the bad classification results may stem from this fact. Apart from the partly limited image set we mainly focused on standard wavelet based textural features known from texture classification. But pit patterns have not yet been proven to exhibit specific textural properties. Additionally the ground truth classification does not rely on visual properties, therefore it is not even clear yet whether visual properties exist which could theoretically be used to correctly classify. Regarding the tests carried out with the Outex images we obtained excellent classification results, especially for the two classes case, where most methods classified all images 93
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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 50 and 51: 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 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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Pit Pattern Classification in Colonoscopy using Wavelets - WaveLab

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