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Introduction to Medical Image Processing

Learn all about the basics of Medical Image Processing in this 3 months course.

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Syllabus

This course is full of best-in-class content by leading faculty and industry experts in the form of videos and projects

Course Overview

This course deals with the core domain of Image processing applied to the healthcare/medical community.
It mainly involves processing and analytical techniques applied to images obtained from a multitude of modalities in the broad spectrum of healthcare.

Course Syllabus

On a daily basis we talk to companies in the likes of Tata Elxsi and Mahindra to fine tune our curriculum.

Module 1 - Introduction to Digital Image Processing

Image Processing Fundamentals
What is a Digital Image?
- Classification of Imaging Systems
- Interaction of Steps in Image Processing
Components of Image Processing System
Image Sensing
- Creation of 2D Image
- Creation of 3D Image
Digital Image Acquisition
Image Sampling and Quantization
Image Quality Metrics
Noise In Image
Digital Imaging and Communications in Medicine (DICOM)
DICOM File Format
Slicer Software Introduction
Reading and Visualizing DICOM Files

Module 2 - Medical Image Formats

What is a Medical Image?
Components of Medical Image
Digital Imaging and Communications in Medicine
DICOM Elements and Objects
Neuroimaging Informatics Technology Initiative
- NIfTI components
Simple ITK - DICOM Data Handling
NiBabel - NIfTI Data Handling
Slicer 3D Visualization
Supplementary Slides

Module 3 - Mathematics for Image Processing

Relationship Between Pixels
Mathematical Tools
Image Transforms
Intensity Transformation
Histogram Processing
Spatial Filtering
Non-Linear Filters
Frequency Domain Filtering

Module 4 - Image Pre-processing

What is a Digital Image?
Gray Scale and Colour Image
Histogram of an Image
Histogram Sliding
Histogram Stretching
Histogram Equalization
Image Enhancement
Image Thresholding
Adaptive Thresholding
Otsu’s Thresholding
What are Morphological Operations?
Mathematical Morphology
Erosion
Dilation
Image Opening and Image Closing
Morphological Gradient
Morphological Algorithms
Boundary Extraction

Module 5 - Image De-noising

Image De-Noising Fundamentals
- What is Noise?
- Noise in Medical Imaging
- Noise Models
Different Types of Noise
- Gaussian Noise
- Impulse Noise - Salt and Pepper Noise
- Poisson Noise
- Speckle Noise
Noise Distributions in Different Modalities
Image Denoising
Image Denoising Techniques
- Linear Filters and Non-linear Filters
- Gaussian Filter
- Mean Filter
- Bilateral Filter
- Non-local Means

Module 6 - Image Texture Extraction and Applications

What is Texture?
Texture Analysis
- Texture Classification
- Texture Segmentation
- Texture Synthesis
- Texture Shape
Challenges in Texture Analysis
Statistical Methods-Based Approach
Gray Level Co-occurrence
Numeric Features of GLCM
Fourier Approach for Texture Descriptor Concept
Autocorrelation Function
Gabor Filter
Gabor Filter Bank
Classification Phase
K-Nearest Neighbor (KNN)
Image Classification Metrics
Image Texture Analysis
Region Based Texture Segmentation
Texture Edge Detection

Our courses have been designed by industry experts to help students achieve their dream careers

Industry Projects

Our projects are designed by experts in the industry to reflect industry standards. By working through our projects, Learners will gain a practical understanding of what they will take on at a larger-scale in the industry. In total, there are 1 Projects that are available in this program.

Internal investigation of the medical images to recognize the features present

The purpose of this project is to explore some simple image enhancement algorithms. It introduces spatial and frequency domain filters, which involve learners applying conceptual knowledge and an algorithm, processing the image and seeing the results.