Microscopy and Biomedical Imaging

Researchers are presented with ever larger volumes of data as a result of the increasing resolution and sensitivity of cameras and the automation of microscopy workflows. This data often contains insights linking structure and function at the molecular, cellular, or tissue level, and it can be used to decipher mechanisms and energetics of biological processes. Scientists need to deal with large datasets spanning a variety of microscopy modalities including bright field, fluorescence, laser confocal, AFM, TEM, and SEM.

MATLAB^® provides a platform not just for microscopy, but for all types of biomedical imaging including cytology, histology, in vivo-imaging, and behavior tracking. This platform is flexible and customizable, enabling you to include your own unique workflows and analytics, and allowing you to integrate with other tools. It scales to deal with large data or to combine different modalities and types of data. As a result, MATLAB has become a platform of choice for researchers seeking to:

• Develop new image processing, statistics, or machine learning techniques not available in commercial software and not packaged with instruments.
• Automate tedious or compute-intensive workflows such as preprocessing, categorizing, and analyzing collections of images or videos. This includes collections too large to fit into memory.
• Leverage available compute resources including multicore computers and GPUs, HPC  systems, and the cloud.
• Deploy analytics and algorithms to large groups of users or collaborators via the web or standalone software.

Community Apps and Toolboxes

• Bio-Formats: Standalone library for life sciences image formats
• CCD Camera Characterization: Characterization of CCD cameras for TEM
• CellAnimation: MATLAB framework for microscopy assays
• CellSegm: High-throughput 3D cell segmentation
• CellTracker: Cell tracking tool
• Fluctuation Microscopy: Electron nanodiffraction pattern analysis for EM
• FocusStack: Two-photon calcium neuronal imaging
• IceImarisConnector: Simple commodity class that eases communication between BITPLANE and MATLAB
• iSMS: Single molecule FRET microscopy
• KiT: Kinetochore motion tracking
• MATLABStarter: A series of startup functions for MATLAB to set up a multi-user environment
• MATrack: Time lapse fluorescent microscopy
• Micro-Manager: Open-source microscopy software
• NeuroPG: Optical pattern generation pipeline
• Phase Stretch Transform: Image feature detection using phase stretch transform
• plusTipTracker: Microtubule dynamics tracking
• QuHAnT: High-throughput quantitative histology
• rFRET: Ratiometric FRET microscopy
• ScanBox: Simultaneous imaging and electrophysiology
• SFMetrics: SFM for biomolecules
• SimToolbox: Structured illumination fluorescence microscopy
• SPM: Platform for functional imaging data
• StimServer: Two-photon calcium neuronal imaging
• TACTICS: High content bioimaging platform
• TLM-Tracker: Time-lapse cell segmentation, tracking, lineage analysis
• u-track: Multiple particle tracking and other capabilities

Videos and Webinars

Technical Articles

• Analyzing High-Resolution Microscopy Images
• Accelerating Development of a New Single-Molecule Localization and Tracking Technique
• Using Image Processing and Statistical Analysis to Quantify Cell Scattering for Cancer Drug Research
• Accelerating Neurological Image Analysis with MATLAB at RIKEN Brain Science Institute
• The Watershed Transform: Strategies for Image Segmentation
• Accessing Data in DICOM files
• Automating Image Registration with MATLAB 

User Stories

• Mitsubishi Tanabe Pharma Develops Data Analysis Tools to Speed Drug Discovery
• Beth Israel Deaconess Medical Center Improves MRI Accuracy
• Canada's Institute for Biodiagnostics Develops an Imaging Tool for Assessing Burn Injuries

Examples and How To

• Correcting Nonuniform Illumination
• Color-Based Segmentation Using K-Means Clustering
• Detecting a Cell Using Image Segmentation
• Motion-Based Multiple Object Tracking
• Batch Processing Image Files in Parallel