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Introduction to Medical Image Processing

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

Book a Class, for FREE


Bhavesh Kumbhare

Dar Al-Handasah Engineering company

Rashtrasant Tukadoji Maharaj Nagpur University

Akash Bhadkumbe


Brahmdevdada Mane Polytechnic

Madhura Joshi


Government Polytechnic Ratnagiri

Surbhi Kadayalwar

Aarvee Consultants

Priyadarshini College of Engineering

Dhananjay Shinde

Hyundai Motor India Ltd

Government Engineering college and reseacrch

Karan Dilip Pawar

Oktal Sydac

father agnel polytechnic vashi

Sumedh Lokhande

Mazameer Design & Engineering Studio

Shri Datta Meghe Polytechnic

Rajashree Kadam

Hinduja Tech

Gokhale Education Society's R. H. Sapat College of Engineering, Management Studies and Research


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.

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

Ratings & Reviews by Learners

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Instructors profiles

Our courses are designed by leading academicians and experienced industry professionals.


1 industry expert

Our instructors are industry experts along with a passion to teach.


20 years in the experience range

Instructors with 20 years extensive industry experience.


Areas of expertise

  • Machine Learning
  • Medical Image Processing

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