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Title page
ABSTRACT
Contents
I. Introduction 13
II. Breast Cancer 16
2.1. Breast Cancer 16
2.1.1. Calcification 17
2.1.2. Mass 22
2.2. Diagnosis Method of Breast Cancer 25
2.2.1. Mammography 25
2.2.2. Ultrasound 27
2.2.3. MRI(magnetic resonance imaging) 28
2.2.4. PET(Positron Emission Tomography) Scan 29
2.3. Database of Mammography 30
2.3.1. DDSM database 30
2.3.2. Samsung mammogram database 33
III. Preprocessing of Mammography Image 36
3.1. Introduction 36
3.2. Adaptive Background Segmentation 37
3.3. Experimental Results 42
3.4. Conclusion 44
IV. Image Enhancement of Mammography 46
4.1. Introduction 46
4.2. Contrast Enhancement of Mammography Image 47
4.3. Robust Image Enhancement in Wavelet Domain 50
4.4. Robust mammogram enhancement using homomorphic filtering 51
4.5. Experimental Results 56
4.6. Conclusion 63
V. Microcalcification Detection 64
5.1. Introduction 64
5.2. ANN(Artificial Neural networks) Classifier 65
5.3. SVM(Support Vector Machine) Classifier 69
5.4. ROI Microcalcification Detection 71
5.5. Microcalcification Detection 73
5.6. Experimental Results 76
5.6.1. Microcalcification detection in ANN classifier 76
5.6.2. Microcalcification detection in SVM classifier 80
5.7. Conclusion 82
VI. Conclusion 83
국문요약 86
References 91
Acknowledgement 95
Table 4-1 : Contrast Improvement Index and standard deviation of noise for high noise mammogram 60
Table 4-2 : Contrast Improvement Index and standard deviation of noise for high noise mammogram 62
Figure 2-1 : Terminal ducts and ductules microcalcification 19
Figure 2-2 : Cyst-like dilated lobules microcalcifications 19
Figure 2-3 : Fragments with irregular contour microcalcifications 20
Figure 2-4 : Miscellaneous microcalcifications 21
Figure 2-5 : Lobulated and circumscribed Mass 24
Figure 2-6 : Architectural distortion and spiculated Mass 25
Figure 2-7 : Examples of DDSM database case A-0002-1 32
Figure 2-8 : Examples of Microcalcification of Samsung database 34
Figure 2-9 : Examples of Microcalcification of Samsung database 35
Figure 3-1 : Example of mammogram image (left) and extracted background area (right) 38
Figure 3-2 : An example of histogram and range(μl, μh) 39
Figure 3-3 : Histogram of variance in the selected block and variance range(бl, бh) 40
Figure 3-4 : An example of noise from the mammogram s background. (a) Background part (b) boundary of breast 42
Figure 3-5 : An example of DDMS original mammogram (a) and the result after the proposed method (b) 43
Figure 3-6 : Boundary of a red block area in Fig. 3-5(a) and the result after the proposed method (b) 43
Figure 4-1 : One dimensional contrast enhancement in wavelet domain 49
Figure 4-2 : Examples of background noise and microcalcification areas (indicated by white arrows). (a) and (b) are high noise (var]40) in background and breast, (c) and (d) 51
Figure 4-3 : Homomorphic filter function for applying to wavelet coefficients 52
Figure 4-4 : Robust contrast enhancement with denoising and modified homomorphic filtering 55
Figure 4-5 : Robust contrast enhancement with denoising and modified homomorphic filtering for high noise mammogram 59
Figure 4-6 : Contrast enhancement for low noise mammography image 61
Figure 5-1 : Total system of our proposed method 65
Figure 5-3 : Statistical properties of the grey-level features (Green Line is normal feature and blue line is microcalcification feature) 72
Figure 5-4 : Flowchart of microcalcification detection 74
Figure 5-5 : Directional filter for edge detection 74
Figure 5-6 : Example of microcalcification detection 77
Figure 5-7 : Example of microcalcification detection 78
Figure 5-8 : FROC curve of microcalcification detection using ANN classifier 79
Figure 5-9 : FROC curve of microcalcification detection using SVM classifier 81
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