Intro; Preface; Acknowledgements; Contents; Acronyms; List of Figures; 1 Basics of Pathological Brain Detection; 1.1 History; 1.2 Brain Diseases; 1.2.1 Neoplastic Disease; 1.2.2 Neurodegeneration; 1.2.3 Cerebrovascular Disease; 1.2.4 Inflammation; 1.2.5 Summary; 1.3 A Standard Computer-Aided Diagnosis System; 1.4 Research Trends; References; 2 Neuroimaging Modalities; 2.1 History of Neuroimaging; 2.1.1 Pneumoencephalography; 2.1.2 Cerebral Angiography; 2.1.3 Computerized Tomography; 2.1.4 Positron Emission Tomography; 2.1.5 Single-Photon Emission Computed Tomography

2.2 Magnetic Resonance Imaging2.2.1 Projectile Risk; 2.2.2 Shimming; 2.2.3 Water and Fat Suppression; 2.2.4 Two Types of Contrast; 2.2.5 Interpretation; 2.3 Other Magnetic Resonance Imaging Modalities; 2.3.1 Diffusion Tensor Imaging; 2.3.2 Functional Magnetic Resonance Imaging; 2.3.3 Magnetic Resonance Angiography; 2.3.4 Magnetic Resonance Spectroscopic Imaging; 2.4 Conclusion; References; 3 Image Preprocessing for Pathological Brain Detection; 3.1 k-Space; 3.2 Image Denoising; 3.2.1 Rician Noise; 3.2.2 Solutions; 3.2.3 Wiener Filter; 3.2.4 Wavelet-Based Denoising; 3.3 Skull Stripping

3.3.1 Software Library at the Oxford Centre for Functional MRI of the Brain3.3.2 Statistical Parametric Mapping; 3.3.3 Other Means; 3.4 Slice Selection; 3.5 Spatial Normalization; 3.5.1 FSL Solution; 3.5.2 Matlab Solution; 3.6 Intensity Normalization; 3.7 Image Enhancement; 3.7.1 Histogram Equalization; 3.7.2 Contrast-Limited Adaptive Histogram Equalization; 3.8 Conclusion; References; 4 Canonical Feature Extraction Methods for Structural Magnetic Resonance Imaging; 4.1 Shape Feature; 4.2 Statistical Measure; 4.2.1 Common Measures; 4.2.2 Statistical Chart; 4.3 Image Moments; 4.3.1 Raw Moments

4.3.2 Central Moments4.3.3 Normalized Central Moments; 4.3.4 Hu Moment Invariants; 4.4 Zernike Moments; 4.4.1 Basic Form of Zernike Moments; 4.4.2 Pseudo Zernike Moment; 4.4.3 Coordinate Transform; 4.4.4 Illustration of Pseudo Zernike Polynomials; 4.5 Gray-Level Co-occurrence Matrix; 4.6 Fourier Transform; 4.6.1 Discrete Fourier Transform; 4.6.2 Discrete Sine and Cosine Transform; 4.7 Fractional Fourier Transform; 4.7.1 Unified Time-Frequency Domain; 4.7.2 Weighted-Type Fractional Fourier Transform; 4.7.3 Sampling-Type Fractional Fourier Transform

4.7.4 Eigendecomposition-Type Fractional Fourier Transform4.8 Entropy; 4.8.1 Shannon Entropy; 4.8.2 Tsallis Entropy; 4.8.3 Renyi Entropy; 4.9 Conclusion; References; 5 Multi-scale and Multi-resolution Features for Structural Magnetic Resonance Imaging; 5.1 Wavelet Transform; 5.1.1 Development of Signal Processing; 5.1.2 Potential Application to Pathological Brain Detection; 5.2 Continuous Wavelet Transform and Discrete Wavelet Transform; 5.2.1 Mathematical Analysis of a Continuous Wavelet Transform; 5.2.2 Koch Curve Example of the Continuous Wavelet Transform; 5.2.3 Discrete Wavelet Transform