• Korean Journal of Audiology
• /
• v.23 no.1
• /
• pp.1-7
• /
• 2019

Self-reported difficulties in speech-in-noise (SiN) recognition are common among tinnitus patients. Whereas hearing impairment that usually co-occurs with tinnitus can explain such difficulties, recent studies suggest that tinnitus patients with normal hearing sensitivity still show decreased SiN understanding, indicating that SiN difficulties cannot be solely attributed to changes in hearing sensitivity. In fact, cognitive control, which refers to a variety of top-down processes that human beings use to complete their daily tasks, has been shown to be critical for SiN recognition, as well as the key to understand cognitive inefficiencies caused by tinnitus. In this article, we review studies investigating the association between tinnitus and cognitive control using behavioral and brain imaging assessments, as well as those examining the effect of tinnitus on SiN recognition. In addition, three factors that can affect cognitive control in tinnitus patients, including hearing sensitivity, age, and severity of tinnitus, are discussed to elucidate the association among tinnitus, cognitive control, and SiN recognition. Although a possible central or cognitive involvement has always been postulated in the observed SiN impairments in tinnitus patients, there is as yet no direct evidence to underpin this assumption, as few studies have addressed both SiN performance and cognitive control in one tinnitus cohort. Future studies should aim at incorporating SiN tests with various subjective and objective methods that evaluate cognitive performance to better understand the relationship between SiN difficulties and cognitive control in tinnitus patients.

• Journal of the Institute of Convergence Signal Processing
• /
• v.12 no.4
• /
• pp.267-273
• /
• 2011

In this paper, we proposed the method to detect brain tumor region in MR images. Our method is composed of 3 parts, detection of tumor slice, detection of tumor region and tumor boundary detection. In the tumor slice detection step, a slice which contains tumor regions is distinguished using symmetric analysis in 3D brain volume. The tumor region detection step is the process to segment the tumor region in the slice distinguished as a tumor slice. And tumor region is finally detected, using spatial feature and symmetric analysis based on the cluster information. The process for detecting tumor slice and tumor region have advantages which are robust for noise and requires less computational time, using the knowledge of the brain tumor and cluster-based on symmetric analysis. And we use the level set method with fast marching algorithm to detect the tumor boundary. It is performed to find the tumor boundary for all other slices using the initial seeds derived from the previous or later slice until the tumor region is vanished. It requires less computational time because every procedure is not performed for all slices.

• The Korean Journal of Nuclear Medicine Technology
• /
• v.24 no.1
• /
• pp.15-19
• /
• 2020

Purpose Generative Adversarial Network(GAN) is one of deep learning technologies. This is a way to create a real fake image after learning the real image. In this study, after acquiring artificial intelligence images through GAN, We were compared and evaluated with real scan time images. We want to see if these technologies are potentially useful. Materials and Methods 30 patients who underwent ¹⁸F-FDG Brain PET/CT scanning at Severance Hospital, were acquired in 15-minute List mode and reconstructed into 1,2,3,4,5 and 15minute images, respectively. 25 out of 30 patients were used as learning images for learning of GAN and 5 patients used as verification images for confirming the learning model. The program was implemented using the Python and Tensorflow frameworks. After learning using the Pix2Pix model of GAN technology, this learning model generated artificial intelligence images. The artificial intelligence image generated in this way were evaluated as Mean Square Error(MSE), Peak Signal to Noise Ratio(PSNR), and Structural Similarity Index(SSIM) with real scan time image. Results The trained model was evaluated with the verification image. As a result, The 15-minute image created by the 5-minute image rather than 1-minute after the start of the scan showed a smaller MSE, and the PSNR and SSIM increased. Conclusion Through this study, it was confirmed that AI imaging technology is applicable. In the future, if these artificial intelligence imaging technologies are applied to nuclear medicine imaging, it will be possible to acquire images even with a short scan time, which can be expected to reduce artifacts caused by patient movement and increase the efficiency of the scanning room.

• Journal of Korea Multimedia Society
• /
• v.15 no.12
• /
• pp.1409-1416
• /
• 2012

An image segmentation result depends on pre-processing steps such as contrast enhancement, edge detection, and smooth filtering etc. Especially medical images are low contrast and contain some noises. Therefore, the contrast enhancement and noise removal techniques are required in the pre-processing. In this study, we present an extension by a novel histogram equalization in which both local and global contrast is enhanced using neighborhood metrics. When checking neighborhood information, filters can simultaneously improve image quality. Most important is that original image information can be used for both global brightness preserving and local contrast enhancement, and image quality improvement filtering. Our experiments confirmed that the proposed method is more effective than other similar techniques reported previously.

• Journal of Sensor Science and Technology
• /
• v.26 no.2
• /
• pp.73-78
• /
• 2017

A tetrode is one of the neural electrodes, and it is widely used to record neural signals in the brain of a freely moving animal. The impedance of a neural electrode is an important parameter because it determines the signal-to-noise ratio of the recorded neural signals. Here, we developed a modification technique using carbon nanotube-polypyrrole composite nanostructures to decrease the impedances of tetrodes. The synthesis of the carbon nanotube and polypyrrole nanostructures was performed in two steps. In the first step, randomly dispersed carbon nanotubes and pyrrole monomers were gathered and aligned on the tetrode electrode. Next, they were electro-polymerized on the electrode surface. As the applied time (step-1 and step-2) and the offset voltage increased, the impedances of the tetrodes decreased. The modification technique is, therefore, an important and useful of lowering the impedances of tetrodes.

• Proceedings of the KOSOMBE Conference
• /
• v.1995 no.11
• /
• pp.189-192
• /
• 1995

In Visually Evoked Potentials(VEP) or Auditory Evoked Potentials(AEP), the components by the stimulation and the components which are irrelevant to the stimulation(noise or nonstationary spontaneous EEG) are mixed together. So one should average hundreds of EP waves to extract the components by the stimulation only. In this study, we have classified EP's, which are the responses of the different stimulations and different states of subjects. To classify the EP waves, the cross-correlation coefficients and neural network method(error back propagation) are used and compared.

• International Journal of Control, Automation, and Systems
• /
• v.5 no.5
• /
• pp.601-605
• /
• 2007

In this paper, we propose a simple and relative electrode contact monitoring method. By exploiting the power line interference, which is regarded as one of the worst noise sources for bio-potential measurement, the relative difference in electrode impedance can be measured without a current or voltage source. Substantial benefits, including no extra circuit components, no degradation of the body potential driving circuit, and no electrical safety problem, can be achieved using this method. Furthermore, this method can be applied to multi-channel isolated bio-potential measurement systems and home health care devices under a steady measuring environment.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.1 no.1
• /
• pp.104-112
• /
• 2001

Simply, Nonlinear dynamics theory means the complicated and noise-like phenomena originated form nonlinearity involved in deterministic dynamical system. An almost all the natural signals have nonlinear property. However, there exist few analysis software tool or package for a research and development of applications. We develop nonlinear time series analysis simulator is to provide a common and useful tool for this purpose and to promote research and development of nonlinear dynamics theory. This simulator is consists of the following four modules such as generation module, preprocessing module, analysis module and ICA module. In this paper, we applied to Electroencephalograph (EEG), as it turned out, our simulator is able to analyze nonlinear time series. Besides, we could get the useful results using the various parameters. These results are used to diagnostic the brain diseases.

• Proceedings of the KOSOMBE Conference
• /
• v.1998 no.11
• /
• pp.305-306
• /
• 1998

In this paper, we infer the onset of the brain infarction from the MR image using evaluate signal intensities on diffusion weighted and turbo spin echo T2-weighted and FLAIR images. Infarcts were divided into four stages (hyperacute, acute, subacute, chronic) depending on period of onset. DWI is useful for the detection of early ischemic infarct, and stages of ischemic infarctions can be estimated by evaluating CR(conspicuity ratio) and CNR(contrast to noise ratio) on DW, T2, FLAIR images Hyperacute infarcts were visualized DWI. Acute infarcts were visualialized both DWI and T2 Weighted image.

• Proceedings of the KOSOMBE Conference
• /
• v.1993 no.11
• /
• pp.151-155
• /
• 1993

Evoked potentials(EP) measured with scalp electrodes are often described as a deterministic process corrupted by uncorrelated electrical activities occuring in the brain and These electrical activities(ongoing EEG) refer to noise in EP recording. The Conventional method to determine the EP waveform requires long recording time. Unfortunately most of algorithm developed are too complicated for implementation in real time. Thus, conner EP recording devices use Ensemble average for real time processing. In this paper introduce EP recording hardware for processing advanced algorithm in real tlne. This hardware is composed of DSP chip(TMS320c25) and microcomputer.