| Course Name | Signal & Image Processing |
| Course Code | 24AIM301 |
| Program | B.Tech. in Artificial Intelligence (AI) and Data Science (DS) in Medical Engineering |
| Semester | V - Micro-credential courses: Set 4 |
| Credits | 4 |
| Campus | Coimbatore |
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.
Time-Frequency Analysis – Fourier transform, wavelet transform, applications of wavelets, Multivariate analysis- PCA and ICA in biomedical signal analysis.
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.
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:
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
| </CO/PO | PO1 | PO2 | PO3 | PO4 | PO5 | PO6 | PO7 | PO8 | PO9 | PO10 | PO11 | PO12 | PSO1 | PSO2 | PSO3 |
| CO1 | 2 | – | – | – | – | – | – | 2 | 2 | 2 | – | 2 | 3 | 1 | 1 |
| CO2 | 2 | 3 | – | – | 2 | – | – | – | 2 | 2 | – | 2 | 2 | 1 | 1 |
| CO3 | 2 | 3 | – | – | 2 | – | – | – | 2 | 2 | – | 2 | 2 | 1 | 1 |
| CO4 | 2 | 3 | – | – | – | – | – | – | 2 | 2 | – | 2 | 1 | 1 | 1 |
| CO5 | 2 | 2 | – | – | – | – | – | – | 2 | 2 | – | 2 | 1 | 1 | 1 |
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