Pathological Brain Detection

Part I Background Chapter 1 Traditional Manual Methods of Pathological Brain Detection Chapter 2 Imaging Modalities-CT, Ultrasound, Magnetic Resonance Imaging (MRI), Magnetic Resonance Spectroscopy (MRS) Chapter 3 A standard Computer-aided Diagnosis (CAD) System Part II CAD over Single Slice Chapter 4 Materials Chapter 5 Fea ture Extraction I: Wavelet Transform, Wavelet Energy, Wavelet Entropy (Shannon or Tsallis?) Chapter 6 Feature Extraction II: Stationary Wavelet Transform, Wavelet Packet, Dual-Tree Complex Wavelet Transform Chapter 7 Feature Extraction III: Ripplet Transform, Hu Moment Invariants Chapter 8 Feature Extraction IV: Fractional Fourier Transform and Fractional Fourier Entropy Chapter 9 Feature Reduction: Principal Component Analysis Chapter 10 Classification I: Decision Tree, Artificial Neural Network, Extreme Learning Machine Chapter 11 Classification II: Support Vector Machine (SVM), Generalized Eigenvalue Proximal SVM, Twin SVM Chapter 12 Optimization I: Swarm Intelligence Method Chapter 13 Optimization II: Hybridization: PSO with BBO and ABC Part III CAD over 3D Brain Chapter 14 Combination of Support Vector Machine and Decision Tree Chapter 15 Eigenbrain Method Chapter 16 3D Discrete Wavelet Transform Chapter 17 Displacement Field Part IV CAD over Spectroscopy Chapter 18 In vivo MRS for autistic disorder Chapter 19 Brain Imaging for Autism Spectrum Disorder Part V Conclusion and Future Research Chapter 20 Conclusion and Future Research