Some of the tasks for medical image analysis are visualization and exploration of 2D images and 3D volumes, segmentation, classification, registration, and 3D reconstruction of image data.

The images for this analysis can be obtained from medical imaging modalities such as x-ray (2D and 3D), ultrasound, computed tomography (CT), magnetic resonance imaging (MRI), nuclear imaging (PET and SPECT), and microscopy.

MATLAB has a development environment and built in analysis and data access functionality for building algorithms for medical image analysis.

For example, you can analyze and detect cancerous anomalies in the cells.

For repetitive or subjective tasks, computational medical image analysis can remove inconsistencies due to human error.

The algorithms can be used to manage illnesses and health risks as well as promote health and wellbeing.

For example, you can load an MRI study of the human brain into the Volume Viewer and explore the data that shows the location and type of tumors found in the brain.

The resulting whole slide images (WSIs) have extremely high resolution.

Reading WSIs is a challenge because the images cannot be loaded into memory and therefore require out-of-core image processing techniques.

MATLAB bigimage objects can store and process this type of large multiresolution image.

For example, you can use the interactive Image Segmenter app to segment bone from soft tissue and further refine the results of an MRI image with different methods.

The Volume Segmenter app offers many ways to explore a volume and segment objects in the volume.

For example, you can load a stack of MRI images of the brain and view the volume slice-by-slice or as a 3D representation.

You can design and train neural networks or use pretrained networks.

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Matlab Volume Viewer