3D image processing is the visualization, processing, and analysis of 3D image data through geometric transformations, filtering, image segmentation, and other morphological operations.
3D image processing is commonly used in medical imaging to analyze DICOM or NIfTI images from radiographic sources like MRI or CT scans. You can also use 3D image processing techniques in microscopy to detect and analyze tissue samples or trace neurons.
Beyond medical imaging, you can use 3D image processing techniques to process security scans of baggage or to analyze scans of materials to understand their structure. Other application areas include video activity recognition for consumer electronics or aerial surveillance for defense systems.
Image Import and Visualization
3D image data can come from a variety of devices and file formats. To effectively import and visualize 3D images, it is important to have access to the underlying data and metadata for the images.
You can visualize 3D images using a variety of methods depending on the details that you want to observe. In some applications, you may want to visualize the 3D data as a rendered volume.
In other applications, you may want to see the 3D data as 2D planes within a three-dimensional coordinate system.
Image Filtering and Enhancement
3D images commonly contain unwanted noise that obscures or deemphasizes the features of the volumes that you are interested in. Applying image filters, normalizing image contrast, or performing morphological operations are common techniques for eliminating noise from 3D images.
When working with datasets of 3D images, the images are commonly taken from different devices, or while a device is moving, which can introduce misalignment through rotation, or skew and scaling differences. You can eliminate or reduce this misalignment using 3D geometric transformations and image registration techniques.
When analyzing a volume or 3D image, you may want to isolate certain regions to perform calculations only on the area of interest. For example, if you want to calculate the volume of a bottle inside a box, you can use image segmentation to partition the 3D image between the bottle and the other structures in the box.
Importing 3D Image Data
With MATLAB, you can use interactive apps or built-in functions to import your 3D image data from a variety of file formats such as TIFF, DICOM, or NIfTI.
Visualizing Volume Data
MATLAB lets you visualize and explore labeled or unlabeled 3D image data.
Registering 3D Images from Different Modalities
MATLAB supports images from a variety of modalities and provides built-in image registration workflows to integrate them.
Image Filtering and Enhancement Operations
With MATLAB, you can reduce noise or enhance images using a variety of image filtering techniques like Gaussian filtering, box filtering, or image morphology.
Segmenting Components of 3D Data
MATLAB provides interactive apps and built-in functions that help you automate 3D image segmentation routines.
This example shows how to perform a 3D segmentation using active contours (snakes). Interactively segment 2D slices of the volume using the Image Segmenter app to create a starting point for the active contour algorithm.
3D Image Processing Using Deep Learning
A deep learning approach to 3D image processing may involve using convolutional neural networks and semantic segmentation to automatically learn, detect, and label relevant features in 3D images.
This example shows how to use MATLAB to train a 3D U-Net network and perform semantic segmentation of brain tumors in 3D images.