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Advanced Medical Image Processing in Clinical Applications
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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 focuses on image processing as it applies 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.
- This course is designed to empower learners who are aspiring to be professionals in the field of medical image processing and explore upcoming trends related to AI application and surgical navigation.
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
- Fundamental steps in image processing
- Components of Image Processing System
- Image sensing and acquisition
- Image Quality Metrics
- MTF (DQE)
- SNR
- Contrast
- SSIM (Structural Similarity Index)
- PSNR
Module 2 - Medical Image Formats
- 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
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 an Digital Image?
- Gray scale and color image
- Histogram & Thresholding techniques
- Otsu’s Thresholding
- Mathematical Morphology
- Erosion
- Dilation
- Image Opening and Image closing
- Morphological Gradient & Algorithm
- Boundary Extraction
Module 5 - Image Denoising
- Image Denoising Fundamentals
- Different types of noise
- Gaussian noise
- Impulse noise - Salt and Pepper Noise
- Poisson noise
- Speckle noise
- Noise distributions in different modalities
- Image Denoising
- Linear filters and Non Linear filters
- Image Denoising Techniques
- Gaussian filter
- Mean filter
- Bilateral filter
- Non local Means
- Image quality metrics
- Mean Squared Error (MSE)
- Root mean squared error
- Peak Signal to Noise Ratio (PSNR)
- Structural Similarity Index (SSIM)
Module 6 - Image Texture Extraction and applications
- What is Texture?
- Texture definition
- Texture Analysis, Classification, Segmentation & synthesis
- 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 , Filter Bank
- Classification phase
- K-nearest neighbour (KNN)
- Region based texture segmentation
- Texture edge detection
- Image classification metrics
Module 7 - Image segmentation
- Image Segmentation
- Thresholding
- Energy Minimization Graph Cuts
- Active Contours
- Clustering
- Evaluating Segmentation Accuracy
Module 8 - Image Registration
- Outline of Image registration
- Modalities and Applications
- Image Registration
- Deformable Image Registration
- Optimization Approach
- Similarity Measures
Module 9 - Convolutional Neural Network
- Overview of Machine learning and Deep Learning basics
- Theory of Convolutional Neural Networks (CNNs)
- Activation Functions
- Regularization
- Optimization
- Learning rates
Module 10 - Image Classification using CNN’s
- Recap of Module 3 Topics
- CNNs for Classification
- CNN Building Blocks
- CNN Architecture
Module 11 - Image segmentation using CNN’s
- CNNs for segmentation
- Sliding windows
- Skip connections
- Dilation
- FCN architecture
- nnU-Net
- UNet architecture
- Medical image segmentation
Module 12 - Practical and Advanced Topics
- Transfer Learning
- Self-Supervised Learning
- SSL for Medical Image Analysis
- FDA regulations for Medical Image Analysis
- Multi-texture Interpolation
- Shaded Iso-surfaces
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 3 Projects that are available in this program.
Internal Investigation of the Medical Images
In this project, the learners are asked to investigate medical images to recognize the features present. For the given dataset, they should perform suitable pre-processing methods to obtain the best results.
Image Registration, Classification & Segmentation using AI Techniques
In this project, the learners are asked to write image processing algorithms to register the given pair of retinal images. They should also evaluate registration performance and write a detailed report describing their method and results.
Magnetic Resonance Images (MRI) Detection and Registration
Our courses have been designed by industry experts to help students achieve their dream careers
Ratings & Reviews by Learners
Skill-Lync has received honest feedback from our learners around the globe.
Flexible Pricing
Talk to our career counsellors to get flexible payment options.
INR 40,000
Inclusive of all charges
Become job ready with our comprehensive industry focused curriculum for freshers & early career professionals
1 Year Accessto Skill-Lync’s Learning Management System (LMS)
Personalized Pageto showcase Projects & Certifications
Live Individual & Group Sessionsto resolve queries, Discuss Progress and Study Plans.
Personalized & Hands-OnSupport over Mail, Telephone for Query Resolution & Overall Learner Progress.
Job-Oriented Industry Relevant Curriculumavailable at your fingertips curated by Global Industry Experts along with Live Sessions.
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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