NPTEL : NOC:Medical Image Analysis (Biotechnology)

Co-ordinators : Prof. Ganapathy Krishnamurthi


Lecture 1 - Medical Image Analysis - Introduction

Lecture 2 - X-ray imaging

Lecture 3 - MRI Physics

Lecture 4 - Magnetic Resonance Image Acquisition

Lecture 5 - Ultrasound Imaging

Lecture 6 - Radionuclide Imaging

Lecture 7 - Basic Image Processing Methods

Lecture 8 - Contrast Enhancement

Lecture 9 - Histogram Equalization

Lecture 10 - Edge Enhancement - Laplacian

Lecture 11 - Noise Reduction

Lecture 12 - Diffusion Filtering

Lecture 13 - Bayesian Image Restoration

Lecture 14 - Registration Introduction

Lecture 15 - Framework

Lecture 16 - Image Coordinates

Lecture 17 - Transforms

Lecture 18 - Metrics

Lecture 19 - NonRigid Registration

Lecture 20 - Demons part - 1

Lecture 21 - Demons part - 2

Lecture 22 - FFDBSplines

Lecture 23 - Endoscopy - Where are we with AI ?

Lecture 24 - Computer vision and DL in the operating room

Lecture 25 - ML in intraoperative tissue identification

Lecture 26 - Basic Image Processing Techniques Using MATLAB

Lecture 27 - Image Registration Using Matlab

Lecture 28 - Basic Image Processing Techniques Using Python

Lecture 29 - Calculus of variations

Lecture 30 - Snakes - Active Contour Models

Lecture 31 - Level Sets, Geodesic Active Contours, Mumford-Shah Functional, Chan-Vese

Lecture 32 - Mumford-Shah Functional, Chan-Vese

Lecture 33 - Segmentation Models Demo [Snakes (Active Contours ) Chan-Vese segmentation, Geodesic active Contour]

Lecture 34 - Active Shape Models

Lecture 35 - Snake tutorial

Lecture 36 - Level Set Method

Lecture 37 - Chan Vese Segmentation

Lecture 38 - Neural Networks Introduction

Lecture 39 - Linear Regression

Lecture 40 - Gradient Descent Formulation

Lecture 41 - Linear Regression Demo

Lecture 42 - Feed forward neural Networks

Lecture 43 - Example with XOR

Lecture 44 - Introduction to CNNs

Lecture 45 - Max Pooling

Lecture 46 - Applications of Cnns

Lecture 47 - CNN Training

Lecture 48 - Semantic Segmentation

Lecture 49 - Classification Demo in Pytorch

Lecture 50 - Generative Models

Lecture 51 - GAN Final Demo