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Image data can usually be described in two dimensions (rows and columns), with possibly an additional dimension for red, green, blue (RGB). However, further dimensions are sometimes required for more accurate and detailed image analysis in specific applications and domains.
For example, you may wish to study a three-dimensional (3D) volume, measure the distance between two parts, or model how that three-dimensional volume changes over time (the fourth dimension). In these cases, you need more than two dimensions to make sense of what you're seeing.
Multidimensional image processing, or n–dimensional image processing, is a broad term for analyzing, extracting, and enhancing useful information from image data with two or more dimensions. It is especially useful and necessary for medical imaging, remote sensing, materials science and microscopy applications.
Some of these applications may involve data from more channels than traditional grayscale, RGB, or red, green, blue, alpha (RGBA) images. Using devices with recognition, filtering, and segmentation capabilities, N-dimensional image processing can help you learn and make informed decisions.
Multidimensional image processing gives you the flexibility to perform traditional 2D filtering functions in scientific applications. Specifically, in medical imaging, computed tomography (CT) and magnetic resonance imaging (MRI) scans require multidimensional image processing to form images of the body and its functions. For example, multidimensional image processing is used in medical imaging to detect cancer or estimate tumor size (Figure 1).
Figure 1: In a multidimensional digital pathology use case, tissue can be drawn and inspected quickly
Challenges for Multidimensional Image Processing Developers
In addition to identifying, acquiring, and storing the image data itself, processing multidimensional image data also faces a series of challenges.
First, multidimensional images are larger in size than 2D images and often have high resolution, so loading them into memory and accessing them is time-consuming.
Second, processing each additional dimension of image data requires additional time and processing power. Analyzing more dimensions expands the scope of consideration.
Third, computer vision and image processing algorithms take longer to analyze each additional dimension, including low-level operations and primitives. The complexity of multidimensional filters, gradients, and histograms grows with each additional dimension.
Finally, dataset visualization for multidimensional image processing becomes more complex due to the extra dimensions considered and the quality that must be presented when manipulating the data. In biomedical imaging, the level of detail required can make the difference in identifying cancer cells and damaged organ tissue.
Multidimensional I/O
If you are a data scientist or researcher working with multidimensional image processing, you need software that can efficiently load and process large image files. Popular multidimensional file formats include:
NumPy binary format (.npy)
Tagged Image File Format (TIFF)
TFRecord （.TFRecord ）
close
Variations of the above format
Because every pixel counts, you must use all available processing power to accurately process image data. Graphics processing unit (GPU) hardware gives you the processing power and efficiency you need to process and balance the workload of analyzing complex multidimensional image data in real time.
cuCIM
Compute Unified Device Architecture Clara IMage (cuCIM) is an open source, accelerated computer vision and image processing software library that leverages the processing power of GPUs to address developers' needs and difficulties in processing multi-dimensional images.
Data scientists and researchers need fast, easy-to-use, reliable software to handle increasing workloads. While tuned specifically for biomedical applications, cuCIM can be used for geospatial, materials and life sciences, and remote sensing use cases.
cuCIM provides more than 200 computer vision and image processing functions for color conversion, exposure, feature extraction, measurement, segmentation, restoration and transformation.
cuCIM is a powerful and fast image processing software that requires minimal changes to existing pipelines. cuCIM gives you enhanced digital image processing capabilities that can be integrated into existing pipelines:
Medicine Open Network for Artificial Intelligence (MONAI)
Numba
NumPy
PyTorch
TensorFlow
You can integrate using the C++ or Python application programming interface (API), which matches OpenSlide for I/O and scikit image for processing in Python.
The cuCIM Python bindings provide many common computer vision and image processing functions that are easily integrated and compiled into a developer's workflow.
Using cuCIM requires no learning of new interfaces or programming languages. In most cases, only one line of code is added to transfer the image to the GPU. The cuCIM encoding structure is nearly identical to that used by the CPU, so few changes are required to take advantage of GPU supported features.
Since cuCIM also supports GPUDirect Storage (GDS), you can efficiently transfer data to and from storage directly to the GPU without creating an intermediate copy in host (CPU) memory. This saves time on I/O tasks.
With its quick setup, cuCIM offers the benefits of GPU-accelerated image processing and efficient I/O with minimal developer effort and no low-level Computing Unified Device Architecture (CUDA) programming.
Source: NVIDIA Enterprise Developer Community
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