https://scholar.dgist.ac.kr/handle/20.500.11750/11760 Wed, 08 Apr 2026 20:21:53 GMT 2026-04-08T20:21:53Z https://scholar.dgist.ac.kr/handle/20.500.11750/56793 Title: 정합필터 기반 신호처리를 활용한 동작 상상 기반 fNIRS 신호의 사용자 상태 구분 Author(s): 이승준; 엄태인; 이옥균 Abstract: Functional near-infrared spectroscopy (fNIRS) has the potential for the application of the brain - computer interface (BCI). Statistical features are often extracted from NIRS measurements to classify a signal in fNIRS-BCI. This paper introduces a matched filter-inspired feature extraction method and compares it to the conventional method. Experimental results demonstrate that the proposed method shows a higher accuracy on average than the conventional method in motor imagery signal classification. Thu, 23 Nov 2023 15:00:00 GMT https://scholar.dgist.ac.kr/handle/20.500.11750/56793 2023-11-23T15:00:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/46835 Title: Likelihood-based bilateral filtration in material decomposition for photon counting CT Author(s): Lee, Okkyun Abstract: The maximum likelihood (ML) principle has been a gold standard for estimating basis line-integrals due to the optimal statistical property. However, the estimates are sensitive to noise from large attenuations or low dose levels. One may apply filtering in the estimated basis sinograms or model-based iterative reconstruction. Both methods effectively reduce noise, but the degraded spatial resolution is a concern. In this study, we propose a likelihood-based bilateral filter (LBF) for the estimated basis sinograms to reduce noise while preserving spatial resolution. It is a post-processing filtration applied to the ML-based basis line-integrals, the estimates with a high noise level but minimal degradation of spatial resolution. The proposed filter considers likelihood in neighbours instead of weighting by pixel values as in the original bilateral filtration. Two-material decomposition (water and bone) results demonstrate that the proposed method shows improved noise-to-spatial resolution tendency compared to conventional methods. © 2022 SPIE. Tue, 14 Jun 2022 15:00:00 GMT https://scholar.dgist.ac.kr/handle/20.500.11750/46835 2022-06-14T15:00:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/46834 Title: Iodine-enhanced Liver Vessel Segmentation in Photon Counting Detector-based Computed Tomography using Deep Learning Author(s): Baek, Sumin; Lee, Okkyun; Ye, Donghye Abstract: Liver vessel segmentation is important in diagnosing and treating liver diseases. Iodine-based contrast agents are typically used to improve liver vessel segmentation by enhancing vascular structure contrast. However, conventional computed tomography (CT) is still limited with low contrast due to energy-integrating detectors. Photon counting detector-based computed tomography (PCD-CT) shows the high vascular structure contrast in CT images using multi-energy information, thereby allowing accurate liver vessel segmentation. In this paper, we propose a deep learning-based liver vessel segmentation method which takes advantages of the multi-energy information from PCD-CT. We develop a 3D UNet to segment vascular structures within the liver from 4 multi-energy bin images which separates iodine contrast agents. The experimental results on simulated abdominal phantom dataset demonstrated that our proposed method for the PCD-CT outperformed the standard deep learning segmentation method with conventional CT in terms of dice overlap score and 3D vascular structure visualization. © 2022 SPIE. Tue, 14 Jun 2022 15:00:00 GMT https://scholar.dgist.ac.kr/handle/20.500.11750/46834 2022-06-14T15:00:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/46833 Title: Deep Learning-based Prior toward Normalized Metal Artifact Reduction in Computed Tomography Author(s): Nam, Jeonghyeon; Ye, Dong Hye; Lee, Okkyun Abstract: X-ray computed tomography (CT) often suffers from scatter and beam-hardening artifacts in the presence of metal. These metal artifacts are problematic as severe distortions in the CT images deteriorate the diagnostic quality in clinical applications such as orthopedic arthroplasty. The normalized metal artifact reduction (NMAR) method effectively reduces the artifacts by normalizing the sinogram with the metal traces through the forward projection of the prior image. Because the prior image is the thresholded CT image with the values of the air and soft tissues replaced, the image is noticeably different from the ideal CT thereby making normalized sinogram not completely flat. In this paper, we propose the novel NMAR method with the deep learning-enhanced prior image which is denoised by learning the relationship between NMAR and clean image without metal artifact. The denoised prior image is then forward projected to correct the sinogram with the metal trace. The experimental results on simulated rat phantom dataset demonstrate that our proposed deep prior NMAR achieves higher structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR) than the original NMAR. © 2022 SPIE. Tue, 14 Jun 2022 15:00:00 GMT https://scholar.dgist.ac.kr/handle/20.500.11750/46833 2022-06-14T15:00:00Z