'Medical Signal Processing' is an elective course offered in M. Tech., in Computer Science and Engineering at School of Engineering, Amrita Vishwa Vidyapeetham.
Medical Imaging Modalities and the need for different modalities (MRI, CT, OCT for Retinal Images, PET, X-Ray, Ultra Sound, Microscopy, Flow Cytometry, Imaging Flow Cytometry, etc. Pre-processing – Image Enhancement – Focus Analysis - Noise reduction (Additive and Speckle Noise) – Image Quality Measures - Domain Transformation: Fourier Domain and Wavelet Domain
Medical Image Segmentation – Threshold Based – Region Growing – Active Contours – Level Set – Graph Partitioning – Deep Learning based Segmentation on 2D or 3D volume of Data Feature Extraction – Morphological Features – Textural Features – SIFT, SURF, MSER, HoG, Feature extraction for 1D Biomedical signals : LPC, MFCC – Deep Features Image Registration and Fusion – Keypoints selection – Keypont Descriptors - Keypoint Matching - Geometric transformations
Classification and Clustering– Examples of image classification for diagnostic/assistive technologies – Traditional and Deep learning based classifiers 3D volume reconstruction – Reconstruction of cell structure from focus stack of images - CT and MRI volume reconstruction – Wavelet based Volume Rendering.
TEXTBOOKS/REFERENCES
- Guide to Medical Image Analysis - Methods and Algorithms, Klaus D. Toennies , in Advances in Computer Vision and Pattern Recognition, 2nd Edition, Springer-Verlag London, DOI: 10.1007/978-1-4471-7320-5, ISBN 978-1-4471-7318-2
- Geoff Dougherty, Medical Image Processing Techniques and application, Springer New York 2011
