Year: 1999
Type: article
Abstract: In this paper, we propose a novel dynamic filter using a multilayer neural network, called the neural filter with spatiotemporal inputs (NFST), for improving image quality of medical low-dose X-ray image sequences. We model the noise in the medical X-ray image sequences to make simulated low-dose X-ray images for training the NFST, and have trained the NFST using them. We have performed experiments on the NFST with not only the simulated low-dose X-ray image sequences but also real low-dose X-ray image sequences. Experimental results have demonstrated that the NFST can effectively remove the actual noise in the medical X-ray image sequences, and the ability of NFST is superior to that of the conventional dynamic filter by the quantitative evaluation of image quality. We expect that medical X-ray systems reduce dosage to the patients effectively using it.
Authors: Kenji Suzuki, Tatsuya Hayashi, Shigeyuki Ikeda, Isao Horiba, Noboru Sugie, Michio Nanki
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W2510400181