Scientists use MATLAB® to analyze microscopy images of all sizes, from standard optical bench images to whole slide analysis. Anatomists, pathologists, microbiologists, and biomedical professionals use MATLAB for all steps of a typical microscopy workflow, including preprocessing, cell counting and classification, cell tracking, tissue segmentation, and disease diagnosis.
MATLAB and Image Processing Toolbox™ integrate with Computer Vision Toolbox™, Statistics and Machine Learning Toolbox™, and Deep Learning Toolbox™, enabling scientists to use a wide array of methods in their research. There is also a strong development community for MATLAB in microscopy, and experts in the field are creating even more tools to help scientists with their specific tasks.
“The algorithms we developed in MATLAB and deployed as applications with help from MathWorks consultants have enabled us to obtain quantitative analysis results, avoid human error, collaborate more effectively, reproduce results reliably, and double the number of feasibility studies completed yearly.”Ryuta Saito, Mitsubishi Tanabe Pharma
Using MATLAB for Microscopy
Microscopy Image Analysis with MATLAB
Scientists use MATLAB for quantitative analysis in microscopy workflows. Scientists can develop these workflows without writing code using apps like the Image Segmenter App or the Color Thresholder App, and then automatically create documented code that replicates the interactive processing. Scientists also use morphological and general image processing to perform common microscopy tasks like cell segmentation, counting, and identification. There is a rich ecosystem of scientists developing tools for microscopy image analysis in MATLAB. For examples, see these tools for microscopy on File Exchange.
Whole Slide Analysis with MATLAB
blockedImage, a new datatype for handling gigapixel whole slide images, was introduced to MATLAB in R2019b. Using this datatype, scientists can perform out-of-core operations on whole slide images using code developed for processing smaller microscopy images. This datatype integrates with Deep Learning Toolbox and enables high throughput whole slide analysis using deep learning. Scientists use MATLAB to predict outcomes, segment tissue, and analyze cancer in whole slide images.
Microscope Instrument Control with MATLAB
Scientists and engineers can use MATLAB for control software on their microscopes as well as image acquisition, and general equipment control. Using a combination of all these tools, scientists can create fully functional microscopes that have high-level onboard image formation and analysis schemes. This can eliminate the need for a large data storage footprint and enable a complete workflow on one instrument.