Technological advances in medical imaging over the last century created new opportunities for noninvasive diagnosis and established medical imaging as an integral part of healthcare systems. One major area representing these advances is the interdisciplinary field of medical image processing.
This fast-developing field covers the broad workflow from raw data acquisition to digital image transmission, which together form the data pipeline of modern medical imaging systems. These systems now provide increasingly higher resolution in spatial and intensity dimensions and faster acquisition times, generating large volumes of high-quality raw image data that must be correctly processed and interpreted to yield accurate diagnoses.
This article outlines the key areas of medical image processing, considers the context of specific imaging modalities, and discusses the main challenges and trends in the field.
Core areas of medical image processing
Many concepts and methods are used to structure the field of medical image processing, each focusing on different facets of its core areas. These facets form three main processes: image formation, image computation, and image management.
Figure 1. Structural classification of thematic types in medical image processing.
The image formation process comprises data acquisition and image reconstruction steps that address mathematical inverse problems. Image computation aims to improve the interpretability of reconstructed images and to extract clinically relevant information. Finally, image management covers compression, archival, retrieval, and transmission of acquired images and derived information.
Image formation
Data acquisition
The first essential step in image formation is acquiring raw imaging data. This data contains the primary information describing the physical quantities captured from internal body structures. These measurements drive all subsequent image processing steps.
Different imaging modalities exploit different physical principles and thus detect different physical quantities. For example, in digital radiography (DR) or computed tomography (CT) the relevant quantity is the energy of incident photons; in positron emission tomography (PET), it is photon energy and detection time; in magnetic resonance imaging (MRI), it is parameters of the radiofrequency signal emitted by excited nuclei; in ultrasound, it is echo parameters.
Regardless of modality, the data acquisition process can be broken down into detection of the physical quantity, conversion to an electrical signal, signal preconditioning, and digitization.
Image reconstruction
Image reconstruction is the mathematical process that forms images from acquired raw data. For multidimensional imaging, the process also includes combining multiple datasets captured at different angles or time steps. This area addresses inverse problems, a fundamental topic in the field. The algorithms for these problems fall broadly into two classes: analytical and iterative.
Typical analytical examples include filtered backprojection (FBP), widely used in tomography; the Fourier transform (FT), central in MRI; and delay-and-sum (DAS) beamforming, essential in ultrasound. These algorithms are efficient in terms of computational requirements and processing time.
However, analytical methods rely on idealized models and have limitations, such as difficulty handling measurement noise statistics and complex imaging system physics.
Iterative algorithms overcome many of these limitations and greatly improve robustness to noise and the ability to reconstruct optimal images from incomplete raw data. Iterative methods typically incorporate system and statistical noise models. Starting from an initial object estimate, they compute projected data based on assumed coefficients and compare these projections to the measured data. The difference defines updates to the object estimate. This process repeats over multiple iterations until a cost function, which measures the discrepancy between the estimated and true mappings, is minimized, yielding the final reconstructed image.
Common iterative approaches include maximum likelihood expectation maximization (MLEM), maximum a posteriori (MAP) methods, algebraic reconstruction techniques (ART), and many other algorithms now widely applied across imaging modalities.
Image computation
Image computation covers computational and mathematical methods applied to reconstructed imaging data to extract clinically relevant information. These methods address enhancement, analysis, and visualization of imaging results.
Enhancement
Image enhancement optimizes a transformed representation of the image to improve interpretability. Techniques fall into spatial-domain and frequency-domain categories.
Spatial-domain methods act directly on image pixels and are particularly useful for contrast optimization. These techniques commonly use logarithmic, histogram, and power-law transforms. Frequency-domain methods apply frequency transforms and are well suited to smoothing and sharpening via various filters.
Using these techniques can reduce noise and inhomogeneity, optimize contrast, enhance edges, remove artifacts, and improve other image properties that are critical for accurate downstream analysis and interpretation.
Analysis
Image analysis is central to image computation and encompasses segmentation, registration, and quantification.
Segmentation partitions an image into meaningful contours corresponding to different anatomical structures. Registration ensures multiple images are correctly aligned, which is essential when analyzing temporal changes or combining images from different modalities. Quantification determines properties of identified structures, such as volume, diameter, composition, and other relevant anatomical or physiological metrics. These processes directly affect the quality of image-based assessments and the accuracy of clinical outcomes.
Visualization
Visualization renders image data into intuitive representations of anatomical and physiological information across defined dimensions. Interactive visualization supports early and intermediate stages of image analysis, for example by assisting segmentation and registration, and presents optimized results in the final stage.
Image management
The final part of medical image processing deals with managing acquired information, including technologies for storage, retrieval, and transmission. Several standards and techniques address different aspects of image management. For example, picture archiving and communication systems (PACS) provide economical storage and access to images from multiple modalities, and the Digital Imaging and Communications in Medicine (DICOM) standard is used for storing and transmitting medical images. Specialized techniques for image compression and streaming enable these tasks efficiently.
Challenges and trends
Medical imaging is a relatively conservative field; translation from research to clinical practice can take more than a decade. Its inherent complexity creates multiple challenges across the scientific disciplines that compose it, which steadily drive the development of new methods. These developments define the main trends observable today in core areas of medical image processing.
Image acquisition benefits from hardware innovations developed to improve raw data quality and enrich information content. Integrated front-end solutions enable faster scan times, finer resolution, and advanced architectures such as combined ultrasound/mammography, CT/PET, or PET/MRI systems.
Fast, efficient iterative algorithms are increasingly replacing analytical methods for image reconstruction. They can substantially improve PET image quality, reduce X-ray dose in CT, and enable compressed sensing in MRI. Data-driven signal models are replacing handcrafted models, offering better solutions to inverse problems from limited or noisy data. Key research areas representing reconstruction trends and challenges include system physics modeling, signal model development, optimization algorithms, and image quality assessment.
As imaging hardware captures larger datasets and algorithms become more complex, there is a growing need for more efficient computing technologies. This challenge is being addressed by more powerful graphics processors and parallel processing techniques, which open new opportunities for translating research into practical applications.
The major trends and challenges related to image computation and management span many topics, some examples of which are shown in Figure 2.
Figure 2. Examples of major thematic trends in contemporary medical image computing.
Ongoing development of new techniques is narrowing the gap between research and clinical application, promoting integration of medical image processing into clinical workflows, and supporting more accurate and reliable imaging results.
Conclusion
Medical image processing is a complex interdisciplinary field spanning mathematics, computer science, physics, and medicine. This article provides a concise, structured framework that represents the field and its main topics, trends, and challenges. Data acquisition is among the first and most important areas, as it defines the initial quality level of raw data used throughout subsequent stages of the medical image processing pipeline.
