Medical image analysis is the process of extracting meaningful information from medical images, often using computational methods. 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.

Medical image analysis can be used to automate or to streamline tasks such as counting and identifying cells in a microscopy image. 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. With computational analysis, you can segment tumor tissues from necrosis or measure oxygen saturation in blood vessels.

With medical image analysis, you can reconstruct a 3D representation from MRI images for calculating organ functions and other diagnostic measures

Medical image analysis algorithms can be applied to large amounts of data, such as digital health data collected from wearable devices. The algorithms can be used to manage illnesses and health risks as well as promote health and wellbeing.

Medical Image Analysis with MATLAB

With MATLAB, you can:

• Visualize and explore 2D images and 3D volumes
• Process very large multiresolution and high-resolution images
• Simplify medical image analysis tasks with built-in image segmentation algorithms
• Use deep learning techniques for classification
• Parse, load, visualize, and process DICOM images

In MATLAB, you can explore 3D volumetric data using the Volume Viewer app. 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.

In digital pathology, whole tissue slides are imaged and digitized. 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.

MATLAB includes apps for segmentation. 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 then segment the 3D volume to label the brain and tumor regions.

With MATLAB, you can also use deep learning methods to perform semantic segmentation of brain tumors from 3D medical images. You can design and train neural networks or use pretrained networks.