Course Syllabus
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.