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Advanced Imaging Magazine

Updated: July 8th, 2008 05:26 PM CDT

Medical Imaging and What Lies Ahead

Fig. 1: A color table generated with the developed genetic algorithm.
Fig. 2 Top: MRI image. Middle: PET image. Bottom: Image created using HSL color space. Hue=MRI, Saturation=CT, Lightness=PET.
Fig. 3. Axillary nerve segmentation during local nerve block. Left panel: unprocessed ultrasound image. Right panel: nerve segmentation. Bottom: close-up of region of interest.
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By Maria Helguera

Portions of this paper were previously presented by Dr. Helguera at the 14th Annual Automated Imaging Association meeting, Feb. 3, 2006. It highlights areas where non-medical imaging technology can be applied to solve medical problems.

Even though the discovery of X-rays by Roentgen happened in 1895, more than 100 years ago, it wasn’t until the early 1960s that computerized analysis of radiological images was possible. Although the mathematical foundation for computed tomography was derived by Radon in 1917, it was not until the early 1970s that computed tomography scanners were initially developed. Since then computers have become an integral part of medical imaging systems and hospitals, being utilized for data acquisition, image generation and display, and image analysis.

In the last two decades medical image processing has evolved dramatically. Broadly, development can be grouped as follows [1]:

  • Image analysis, where the input to a system is an image and the output is some sort of measurement. This group includes processes as automated detection and diagnosis.
  • Image processing, where the input to the system is an image and the output is another image with similar contents but differences in appearance. This group includes image enhancement, restoration, compression, registration, etc.
  • Image understanding, where the input is an image and the output is a different level of description such as transforms and pixel mappings.

Over the years medical imaging modalities have multiplied and their use has been accompanied by an increase in the diversity and complexity of associated issues that require more advanced techniques for their solution. Consider for example nuclear medical imaging, where some of the limitations include noise due to scattering, attenuation along propagation through the body, etc., or the widely used digital mammography that requires higher levels of lossless data compression. The use of multiple modalities for improved diagnosis requires the combination of multiple images of the same organ and the ability to display them in a comprehensive way.

Today, medical imaging systems are capable of revealing organs and identify diseases. We can see blood flowing through arteries and veins, cells dying in a tumor, antibodies battling infection and sensations in the brain, to mention just a few applications. These systems may analyze the images they produce, identify malignancies, evaluate plaque deposits, measure bone loss, etc. They help surgeons tracking the position of their instruments in real time. These advances are based on breakthroughs in several disciplines, including computer technology, materials science, and molecular biology, among others.

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