Syllabus

Introduction of Biomedical Signals and Images: ECG, EEG, Imaging Modalities: Survey of major modalities for medical imaging: ultrasound, X-ray, CT, MRI, PET, and SPECT, MRI, FMRI, Various Applications

Fundamentals of Deterministic Signal and Image Processing: Details on Data Acquisition approaches for biomedical signals-Digital Filtering for biomedical signals- DTFT: The discrete-time Fourier transform and its properties. FIR filter design using windows. DFT: The discrete Fourier transform and its properties, the fast Fourier transform (FFT),the overlap-save algorithm, digital filtering of continuous-time signals-Sampling Revisited: Sampling and aliasing in time and frequency, spectral analysis-Image processing I: Extension of filtering and Fourier methods to 2-D signals and systems- Image processing II: Interpolation, noise reduction methods, edge detection, homomorphic filtering- Introduction to wavelets, Time frequency representation, Discrete wavelet transform, pyramid algorithm, Comparison of Fourier transform and wavelet transform, Speech analysis–Cepstrum- Homomorphic filtering of speech signals, ECG signal characteristics–EEG analysis..

Intelligent techniques for biomedical signal analysis: Blind source separation: Use of principal component analysis (PCA) and independent component analysis (ICA) for filtering. Deep learning for one dimensional biomedical signal analysis. Machine learning methods for biomedical

Image Segmentation and Registration: Image Segmentation: statistical classification, morphological operators, connected components. Image Registration I: Rigid and non-rigid transformations, objective functions. Image Registration II: Joint entropy, optimization methods