MATLAB Medical Imaging Toolbox™ User's Guide

✍ Scribed by The MathWorks, Inc.

• Publisher: The MathWorks, Inc.
• Year: 2022
• Tongue: English
• Leaves: 144
• Category: Library

✦ Table of Contents

Getting Started
Medical Imaging Toolbox Product Description
Read, Process, and Write 3-D Medical Images
Get Started with Medical Image Labeler
Start New Labeling Session
Load Image Data
Create Label Definitions
Label Image
Export Labeling Results
How Medical Image Labeler Manages Ground Truth Labels
Medical Image Coordinate Systems
Patient Coordinate System
Intrinsic Coordinate System
Introduction to Medical Imaging
Common Medical Imaging Modalities
Typical Workflow for Medical Image Analysis
Import, Export, and Spatial Referencing
Read, Process, and View Ultrasound Data
Display and Volume Rendering
Visualize 3-D Medical Image Data Using Medical Image Labeler
Display 3-D Medical Image Data in Patient Coordinate System
Display Labeled Medical Image Volume in Patient Coordinates
Create STL Surface Model of Femur Bone for 3-D Printing
Image Preprocessing and Augmentation
Medical Image Preprocessing
Background Removal
Denoising
Resampling
Registration
Intensity Normalization
Preprocessing in Advanced Workflows
Medical Image Registration
Scenarios for Medical Image Registration
Functions for Medical Image Registration
Register Multimodal Medical Image Volumes with Spatial Referencing
Medical Image Labeling
Label 2-D Ultrasound Series Using Medical Image Labeler
Label 3-D Medical Image Using Medical Image Labeler
Collaborate on Multi-Labeler Medical Image Labeling Projects
Create Label Definitions and Assign Data to Labelers (Project Manager)
Label Data and Publish Labels for Review (Labeler)
Inspect Labeled Images (Reviewer)
Export Ground Truth Data and Send to Project Manager (Labeler)
Collect, Merge, and Create Training Data (Project Manager)
Medical Image Segmentation
Create Datastores for Medical Image Semantic Segmentation
Medical Image Ground Truth Data
Datastores for Semantic Segmentation
Convert Ultrasound Image Series into Training Data for 2-D Semantic Segmentation Network
Create Training Data for 3-D Medical Image Semantic Segmentation
Segment Lungs from CT Scan Using Pretrained Neural Network
Brain MRI Segmentation Using Pretrained 3-D U-Net Network
Breast Tumor Segmentation from Ultrasound Using Deep Learning