Abstract
Medical doctors and clinical technologists operate specific, complicated diagnostic systems to assess venous diseases. Instead of using such expensive equipment, low-cost infrared cameras can capture vein images noninvasively and simply. However, the recorded image has a possibility to result in low contrast and a low signal-to-noise (S/N) ratio. An effective image filtering method to estimate venous changes will solve this problem and enable the early detection of disease. For this study, a novel filtering method based on the genetic algorithm (GA) with the expectation–maximization algorithm was proposed for the visualization of vein shapes; its effectiveness was evaluated by images acquired from a near-infrared (780 nm) camera. The novel filter was able to be automatically designed by the GA to improve the worse S/N ratio of vein images, with an unknown correct answer image. If the proposed filtering method is incorporated into e-healthcare applications, it could be widely distributed through smartphones or tablets and facilitate finding abnormal veins at an early stage.
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This study was partially funded by a Grant-in-Aid for Scientific Research (C) from Japan Society for the Promotion of Science (KAKENHI, 25330171).
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Kashihara, K., Iwase, M. Automatic design of an effective image filter based on an evolutionary algorithm for venous analysis. Netw Model Anal Health Inform Bioinforma 5, 1 (2016). https://doi.org/10.1007/s13721-015-0108-z
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DOI: https://doi.org/10.1007/s13721-015-0108-z