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 …
Biomedical · Course
Advanced Medical Image Processing in Clinical Applications
Advanced Medical Image Processing in Clinical Applications
- Duration
- 12 weeks
- Content
- 21 hours
- Projects
- 3
- Mode
- Online
OverviewWhat you'll
What you'll
be able to do.
- 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.
Tools you'll use
Python
Curriculum12 modules,
12 modules,
built with industry.
01Advanced Medical Image Processing in Clinical Applications
- Module 1 - Introduction to Digital Image ProcessingW01
- 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 FormatsW02
- 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 ProcessingW03
- Relationship between Pixels
- Mathematical Tools
- Image Transforms
- Intensity Transformation
- Histogram Processing
- Spatial Filtering
- Non-Linear filters
- Frequency Domain Filtering
- Module 4 - Image Pre-processingW04
- 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 DenoisingW05
- 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 applicationsW06
- 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 segmentationW07
- Image Segmentation
- Thresholding
- Energy Minimization Graph Cuts
- Active Contours
- Clustering
- Evaluating Segmentation Accuracy
- Image Segmentation
- Module 8 - Image RegistrationW08
- Outline of Image registration
- Modalities and Applications
- Image Registration
- Deformable Image Registration
- Optimization Approach
- Similarity Measures
- Module 9 - Convolutional Neural NetworkW09
- 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’sW10
- Recap of Module 3 Topics
- CNNs for Classification
- CNN Building Blocks
- CNN Architecture
- Module 11 - Image segmentation using CNN’sW11
- CNNs for segmentation
- Sliding windows
- Skip connections
- Dilation
- FCN architecture
- nnU-Net
- UNet architecture
- Medical image segmentation
- CNNs for segmentation
- Module 12 - Practical and Advanced TopicsW12
- Transfer Learning
- Self-Supervised Learning
- SSL for Medical Image Analysis
- FDA regulations for Medical Image Analysis
- Multi-texture Interpolation
- Shaded Iso-surfaces
- Module 1 - Introduction to Digital Image ProcessingW01
Industry projects3 projects
3 projects
you'll ship.
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.
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 wr…
Magnetic Resonance Images (MRI) Detection and Registration
In this project, the learners are asked to write an essay on the fMRI Machine. They should also create fMRI data and perform data analysis process to map the functional data with a…
FeeOne fee.
One fee.
Everything included.
Become job ready with our comprehensive industry focused curriculum for freshers & early career professionals
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- Personalized & Hands-On
- Job-Oriented Industry Relevant Curriculum
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