Learn · Medical Image Analysis
Lecture 6 of 51Week 1
RADIONUCLIDE IMAGING
Prof. Ganapathy krishnamurthi · IIT Madras
Credits and licence
- From
- Medical Image Analysis, IIT Madras (at NPTEL)
- Licence
- CC BY-NC-SA, as NPTEL states it. Non-commercial use only.
- Attribution
- NPTEL states: "Distributed under Creative Commons Attribution-ShareAlike / CC BY - NC - SA", beside a BY-NC-SA badge.
- Who can watch
- Tweens profiles and older. How courses are placed
All the lectures
- Introduction
- X-ray imaging
- MRI Physics
- MAGNETIC RESONANCE IMAGE ACQUISITION
- ULTRASOUND IMAGING
- RADIONUCLIDE IMAGING
- Basic Image Processing Methods
- Contrast Enhancement
- Histogram Equalization
- Edge Enhancement - Laplacian
- Noise Reduction
- Diffusion Filtering
- Bayesian IMAGE RESTORATION
- Registration Introduction
- Framework
- Image Coordinates
- Transforms
- Metrics
- NonRigid Registration
- Demons part - 1
- Demons part - 2
- FFDBSplines
- Endoscopy -Where are we with AI ?
- Computer vision & DL in the operating room
- ML in intraoperative tissue identification
- Basic Image Processing Techniques Using MATLAB
- Image Registration Using Matlab
- Basic Image Processing Techniques Using Python
- Calculus of variations
- Snakes - Active Contour Models
- LEVEL SETS, GEODESIC ACTIVE CONTOURS, MUMFORD-SHAH FUNCTIONAL, CHAN-VESE
- MUMFORD-SHAH FUNCTIONAL, CHAN-VESE
- Segmentation Models Demo [Snakes (Active Contours ) Chan-Vese segmentation, Geodesic active Contour]
- ACTIVE SHAPE MODELS
- Snake tutorial
- Level Set Method
- Chan Vese Segmentation
- Neural Networks Introduction
- Linear Regression
- Gradient Descent Formulation
- Linear Regression Demo
- Feed forward neural Networks
- Example with XOR
- Introduction to CNNs
- Max Pooling
- Applications of Cnns
- CNN Training
- Semantic Segmentation
- Classification Demo in Pytorch
- Generative Models
- GAN FinalDemo