Computer Software Accurately Obtains and Evaluates Images of Flow Fields for Medical and Scientific Applications
This diagnostic imaging segmentation uses computer software to measure and analyze flow fields for the prediction and correction of faulty pathways. Flow fields are particularly useful to study in the medical and scientific industries due to the extensive flow fields present in the body, such as blood vessels. Existing techniques for image segmentation either require too much human input or lack the accuracy required for medical diagnoses. Fields requiring the prediction and correction of faulty flow field pathways need an image segmentation method that is autonomous, fast and accurate. Researchers at the University of Florida have developed a diagnostic imaging segmentation that accurately measures, analyzes, and predicts flow field patterns. The imaging segmentation uses autonomous computer software to study and predict random patterns in flow fields, which would allow for the correction of faulty pathways. Examples of technologies that stand to benefit include satellite imaging for geology and defense as well as medical imaging. Satellite imaging could autonomously detect roads, rivers, and footpaths; medical imaging could detect blood vessels in visible-light-camera images or slices of 3D imaging sensors as a precursor to producing flow analyses. The diagnostic imaging would be particularly useful for evaluating disease status or therapeutic responses of pharmaceuticals in treating diseases affecting the vascular system, or the eye, heart, or brain.
Image segmentation that measures, analyzes, and predicts flow fields for medical and scientific purposes
- Uses autonomous computer software, eliminating the need for human touch or interaction to obtain results
- Provides fast, accurate image segmentation of flow fields, increasing the accuracy of diagnostic imaging and providing medically-acceptable diagnoses
- Measures and analyzes images, resulting in predictive capabilities for correcting faulty and erroneous pathways
This image segmentation uses computer software to study areas of interest in a flow field. University of Florida Researchers developed an algorithm that obtains accurate image segmentation of flow fields unachievable through available techniques. Images are processed by the computer software to provide predictive capabilities to analysts concerning flow field patterns and pathways. The algorithm works by detecting objects shaped like pathways, and then takes advantage of geometric knowledge to correct for errors in this detection. The segmentation allows for the comparison of flow fields in addition to the correction of faulty patterns and would provide sufficient segmentation to be deemed acceptable for medical diagnoses.