Unit-1

Review of Biomedical Signals and Systems Introduction to Biomedical signals and characteristics of dynamic biomedical signals, Noises, Filters- IIR and FIR filters, Spectrum – power spectral density function, cross-spectral density and coherence function, cestrum and homomorphic filtering.

Unit-2

Time-Frequency Analysis – Fourier transform, wavelet transform, applications of wavelets, Multivariate analysis- PCA and ICA in biomedical signal analysis.

Unit-3

Fundamentals of Digital Image Processing Components of an image processing system, Digital image representation, Digital images, Image sampling and quantization, Image Enhancement and Segmentation- Segmentation based on dissimilarities (point, line and edges), region-based segmentation (thresholding, region growing, splitting and merging, active contours, clustering, Applications in medical image segmentation, performance evaluation of segmentation algorithms. Feature Extraction and Classification of Medical Images Boundary preprocessing and features, region-based features, texture analysis, principal components, pattern classification and performance evaluation.

Unit-4

Image Compression Coding Redundancy, Spatial and Temporal Redundancy, Irrelevant Information, Measuring Image Information, Shannon’s First Theorem, Fidelity Criteria, Image Compression Models, The Encoding or Compression Process, lossy and lossless image compression techniques.

Course Objectives:

1. To provide students information about advanced biomedical signal processing techniques.
2. To afford the ability to implement and apply techniques for biomedical signal processing and analysis.
3. To impart the basics of digital image processing techniques.
4. To impart an understanding on the application of digital image processing techniques on medical image processing.
5. To enable the students to implement and apply image processing techniques for image quality improvement and analysis of medical images.

Course Outcomes:
After completing this course, students should be able to
CO1: Analyze time and frequency properties of signals using Fourier and Wavelet transforms
CO2: Design filters for processing of signals
CO3: Perform data driven representation of signals using PCA and ICA
CO4: Carry out digital image processing, in various stages (sampling, segmentation, classification)
CO5: Demonstrate competence in image compression and feature extraction

CO-PO mapping

1. ‘Digital Image Processing using MATLAB’, Rafael C. Gonzalez, Richard E. Woods and Steven Eddins, Pearson Education Inc., 2011.
2. ‘Digital Image Processing’, William K. Pratt, John Wiley, New York, 2002.
3. ‘Digital Signal and Image Processing The Sparse Way’, K.P.Soman and R. Ramanathan,Cengage Learning Pvt. Ltd, 2016.
4. Cohen, A. (1986). Biomedical Signal Processing: Volume 1 and 2. CRC Press.
5. Rangayyan, R. M. (2015). Biomedical Signal Analysis. Germany: Wiley.
6. Tompkins, W. J. (1993). Biomedical Digital Signal Processing. United Kingdom: Prentice Hall.
7. Rao, R. M. (1998). Wavelet Transforms: Introduction to Theory and Applications. India: Pearson Education.
8. Biomedical Signal Processing and Control, Journal (ISSN: 1746-8094), Elsevier.
9. Woods, R. E., Gonzalez, R. C. (2018). Digital Image Processing. United Kingdom: Pearson.
10. Jain, A. K. (1989). Fundamentals of Digital Image Processing. India: Prentice Hall.