Dec 1999· IEEE Pulse· Vol 2, pp. 60-70· 398 citations· ⚡ 29 influential· 67 references
ChemistryMathematicsComputer Science
Abstract
Since the discovery of the X-ray radiation by Wilhelm Conrad Roentgen in 1895, the field of medical imaging has developed into a huge scientific discipline. The analysis of patient data acquired by current image modalities, such as computerized tomography (CT), magnetic resonance tomography (MRT), positron emission tomography (PET), or ultrasound (US), offers previously unattained opportunities for diagnosis, therapy planning, and therapy assessment. Medical image processing is essential to leverage this increasing amount of data and to explore and present the contained information in a way suitable for the specific medical task. In this tutorial, we will approach the analysis and visualization of medical image data in an explorative manner. In particular, we will visually construct the image processing algorithms using the popular graphical data-flow builder MeVisLab, which is available as a free download for noncommercial research. We felt that it could be more interesting for the reader to see and explore examples of medical image processing that go beyond simple image enhancements. The part of exploration, to inspect medical image data and experiment with image-processing pipelines, requires software that encourages this kind of visual exploration.
The improvement techniques for X-ray images based on the spatial domain include gamma modification, balance enhancement of contrast, image invert/compliment, super-resolution, contrasting-limited adaptive histogram equalization, and improvement with spatial filtering.
Kawther Seaad· International Journal of Adv...· 0 citations
In the future, MRI is expected to achieve breakthroughs by combining artificial intelligence, developing new contrast agents, and upgrading hardware, further expanding its applications in the medical field.
Jun-Wen Chen, Xin-Yi Liu, Yanghongyu Qian et al.· 0 citations
The application of IVIM-DWI MRI (intravoxel incoherent motion diffusion-weighted imaging) MRI (magnetic resonance imaging) in the evaluation of abnormal enhancement of the margin of hepatocellular carcinoma (HCC) after radiofrequency ablation (RFA).
Yu-Chang Yan, Xue-Chao Du, Yu-Fei Lian et al.· Current medical imaging· 0 citations
The applicability and other aspects of ultra-weak magnetic field imaging are introduced, highlighting its advantages over traditional medical imaging methods and its importance in safeguarding human health is emphasized.
Wen-Hao Li· International Journal of Pub...· 0 citations
This study demonstrates the feasibility of DE CBCT technology on the HyperSight imaging system through a novel statistical material specific imaging algorithm and VMI-based CNR improvements and metal artifact reduction.
The project is the medical diagnostic system, powered by AI and based on the Convolutional Neural Networks (CNNs), that automatically identifies abnormalities in medical images with a high precision that is expected to enhance the accuracy of the diagnosis, ease the workload of the medical specialists, and offer scalab...
B. Aravind, Budati, Sheela Koushi et al.· 0 citations
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