• Journal of the Korean Society of Radiology
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• v.16 no.5
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• pp.575-583
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• 2022

Magnetic resonance diffusion tensor imaging (DTI) has revealed the disruption of brain white matter microstructure in normal aging and alcoholism undetectable with conventional structural MR imaging. we plan to analyze the FA measurements of the ROI of dangerous drinkers selected from Alcohol Use Disorders Identification Test (AUDIT) and Tract-Based Spatial Statics (TBSS) tool was used to extract FA values in the ROI from the image acquired through the pre-processing process. TBSS has a higher sensitivity of the FA value and MD value in the white matter than the brain gray matter, and has the advantage of quantitatively deriving the unlimited degree of brain nerve fibers, and more specialized in the brain white matter. We plan to analyze the fractional anisotropy (FA) measurement value for damage by selecting the center of the anatomical structure of the white matter region of the brain with high anisotropy among the brain neural networks that are particularly vulnerable to alcohol as the region of interest (ROI). In this study, we expected that alcohol causes damage to the brain white matter microstructure from FA value in various areas including both Choroid plexus. Especially, In the case of the moderate drunker, the mean value of FA in Lt, Rt. Choroid plexus was 0.2831 and 0.2872, whereas, in the case of the severe drunker, the mean value of FA was 0.1972 and 0.1936. We found that the higher the score on the AUDIT scale, the lower the FA value in ROI region of the brain white matter. Using the AUDIT scale, the guideline for the FA value of DTI can be presented, and it is possible to select a significant number of potentially severe drinkers. In other words, AUDIT was proved as useful tool in screening and discrimination of severe drunker through DTI.

• Journal of Biomedical Engineering Research
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• v.37 no.2
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• pp.84-89
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• 2016

In this study, we proposed ameliorated method for region of interest (ROI) study to improve its accuracy using partial volume effect (PVE). PVE which arose in volumetric images when more than one tissue type occur in a voxel, could be used to reduce an amount of gray matter and cerebrospinal fluid within ROI of diffusion tensor image (DTI). In order to define ROIs, individual b0 image was spatially aligned to the JHU DTI-based atlas using linear and non-linear registration (http://cmrm.med.jhmi.edu/). Fractional anisotropy (FA) and mean diffusivity (MD) maps were estimated by fitting diffusion tensor model to each image voxel, and their mean values were computed within each ROI with PVE threshold. Participants of this study consisted of 20 healthy controls, 27 Alzheimer's disease and 27 normal-pressure hydrocephalus patients. The result showed that the mean FA and MD of each ROI were increased and decreased respectively, but standard deviation was significantly decreased when PVE was applied. In conclusion, the proposed method suggested that PVE was indispensable to improve an accuracy of DTI ROI study.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.01a
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• pp.273-276
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• 2021

본 논문에서는 사용자의 관심영역(Region of interests, ROI)를 기반 스크롤을 통해 데이터를 좀 더 빠르고 효율적으로 검색할 수 있는 사용자-데이터 인터페이스를 제안한다. 사용자가 관심있는 정보나 콘텐츠를 찾는 행동에서 착안한 우리의 접근 방식은 주어진 콘텐츠에서 ROI를 효율적으로 계산하고, GMM(Gaussian mixture model, 가우시안 혼합 모델)에서 착안해 개발한 커널을 기반으로 사용자가 관심 있어 하는 정보의 위치로 부드럽고 빠르게 화면을 이동시켜 정보를 탐색한다. 과정을 설명하기 앞서, 다수의 ROI가 있을 때 스크롤의 현 위치는 항상 두 ROI의 사이에 있다. 그 두 사이의 거리가 가장 짧은 두 ROI에 각각 우리의 커널을 적용하면 현 위치에서 스크롤 가속에 적용 가능한 두 개의 관성이 생긴다. 여기에 선형 보간법(Linear interpolation)을 적용하여 한층 부드러운 하나의 관성으로 만들고, 이것을 스크롤에 적용한다. 결과적으로, 오직 사용자의 입력에 따라 정보가 검색되는 기존의 접근법과는 달리, ROI와 DOI(Degree of interests, 중요도)를 기반으로 향상된 스크롤을 통해 사용자가 관심 있어 하는 정보나 콘텐츠를 보다 쉽게 직관적으로 찾아줄 수 있기 때문에 사용자는 탐색 시간을 절약할 수 있다.

• Proceedings of the Korea Information Processing Society Conference
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• 2010.04a
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• pp.478-481
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• 2010

컴퓨터 비전에서 객체의 인식, 추적에 앞서 배경으로부터 전경을 분리하는 배경차감 기법과 분리된 전경에 대한 관심 영역(ROI)을 추출하는 것은 일반적인 방법이다. 하지만 전경을 정확히 분리하지 못하면 개별 객체의 관심영역(ROI) 역시 잘못 추출되는 문제가 발생된다. 본 논문에서는 정확하지 않은 전경 분리로 부터 발생되는 개별 객체에 대한 분산된 관심영역을 병합하는 방법을 제안한다. 본 방법은 배경과 분리된 전경에서 한 객체의 일정 거리 이내에 있는 다른 객체를 가상으로 병합하는 단계, 워터쉐드 분할 알고리즘을 적용하는 단계를 거쳐 다시 블럽 레이블링을 수행한다. 제안 방법을 통하여 배경 모델에서 분리된 개별 객체의 병합된 관심영역을 제공한다. 실험에서 기존의 일반적인 블럽 레이블링 방법만을 적용하여 추출한 전경영역과 제안하는 방법에 의한 전경영역을 비교하여 배경 모델에서 분리된 개별 객체의 관심영역이 효과적으로 추출되는 것을 보인다.

• The KIPS Transactions:PartB
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• v.15B no.4
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• pp.259-268
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• 2008

In an effort to increase the data capacity of one-dimensional symbology, 2D barcodes have been proposed a decade ago. In this paper, we present an effective 2D barcode detection algorithm from gray-level images, especially for the handheld 2D barcode recognition system. To locate the symbol inside the image, a criteria based on the block contrast is adopted, and a gray-scale projection with sub-pixel operation is utilized to segment the symbol precisely from the region of interest(ROI). Finally, the segmented ROI is normalized using the inverse perspective transformation for the following decoding processes. We also introduce the post-processing steps for decoding the QR-code. The proposed method ensures high performances under various lighting/printing conditions and strong perspective deformations. Experiments shows that our method is very robust and efficient in detecting the code area for the various types of 2D barcodes in real time.

• The KIPS Transactions:PartB
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• v.10B no.3
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• pp.339-346
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• 2003

In this paper, we propose a new method in which interpolation images are created by using a small number of axiai T2-weighted images instead of using many sectional images for 3D visualization of brain MR images. For image Interpolation, an important part of this process, we first segment a region of interest (ROI) that we wish to apply 3D reconstruction and extract the boundaries of segmented ROIs and MBR information. After the image size of interpolation layer is determined according to the changing rate of MBR size between top slice and bottom slice of segmented ROI, we find the corresponding pixels in segmented ROI images. Then we calculate a pixel's intensity of interpolation image by assigning to each pixel intensity weights detected by cube interpolation method. Finally, 3D reconstruction is accomplished by exploiting feature points and 3D voxels in the created interpolation images.

• Journal of Broadcast Engineering
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• v.11 no.1 s.30
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• pp.54-65
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• 2006

In this paper, we propose a new functionality to Scalable Video Coding (SVC), which is the support of multiple ROIs for heterogeneous display resolution. Scalable video coding is targeted at giving temporal, spatial, and quality scalability for the encoded bit stream. Region of interest (ROI) is an area that is semantically important to a particular user, especially users with heterogeneous display resolutions. The bitstream containing the ROIs could to be extracted without any transcoding operations, which may be one of way to satisfy QoS. To define multiple ROI in SVC, we adapted FMO, a tool defined in H.264, and based on it, we propose a way to encode and decode ROIs. The proposed method is implemented on the JSVM1.0 and the functionality is verified using it.

• Journal of Broadcast Engineering
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• v.13 no.1
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• pp.34-42
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• 2008

This paper proposes the mechanism which improves the qualify of video on a limited network bandwidth by applying the ROI technique to an H.264 Scalable Extension technique. The network environment assumed in this parer is the next generation network convergence environment in which the mobile device has one or more network interfaces. Therefore, we allocate the priority to video packets as the hierarchy structure of H.264 SVC-encoded video stream and ROI information, and transmit those packets over appropriate network channel for using those multiple network interfaces. This paper shows two experiments first one is extracting and allocating the video stream on an appropriate network channel, second one is unequal packet transmission by allocated priorities (e.g. ROI). Performance evaluations show that this approach delivers an improved decoded video quality when compared with conventional transmission schemes, especially on device which has multiple network interfaces.

• Korean Journal of Construction Engineering and Management
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• v.25 no.2
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• pp.36-44
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• 2024

This paper presents the application of Convolutional Neural Networks (CNNs) and Region of Interest (ROI) techniques for concrete crack analysis. Surfaces of concrete structures, such as beams, etc., are exposed to fatigue stress and cyclic loads, typically resulting in the initiation of cracks at a microscopic level on the structure's surface. Early detection enables preventative measures to mitigate potential damage and failures. Conventional manual inspections often yield subpar results, especially for large-scale infrastructure where access is challenging and detecting cracks can be difficult. This paper presents data collection, edge segmentation and ROI techniques application, and analysis of concrete cracks using Convolutional Neural Networks. This paper aims to achieve the following objectives: Firstly, achieving improved accuracy in crack detection using image-based technology compared to traditional manual inspection methods. Secondly, developing an algorithm that utilizes enhanced Sobel edge segmentation and ROI techniques. The algorithm provides automated crack detection capabilities for non-destructive testing.

• Journal of the Korean Society of Radiology
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• v.14 no.5
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• pp.715-723
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• 2020

Cross-talk artifacts occur in two adjacent groups of axial imaging of lesions lumbar 4-5 and sacrum 1 in lumbar spine MRI. This causes problems in reading lesions in areas corresponding to the posterior vertebra. In this study, we are going to completely remove the cross-talk artifacts through optimal concatenation TR. The region of interested were measured by averaging them into fat (ROI1), erector spinal muscle(lateral tract: iliocostalis lumborum muscle) (ROI2), erector spinal muscle(lateral tract: longissimus muscle) (ROI3), and spinous process (ROI4). The mean signal intensity (SI) was 163.43 ± 25.08 at C4 for ROI1, ROI 2 and ROI 3 at C6, 67.89 ± 11.75 and 69.99 ± 10.91 and ROI4 at C5, respectively (p<0.000). The mean signal to noise ratio (SNR) was 135.45 ± 35.90, 56.92 ± 15.90, 58.77 ± 15.59, and 54.91 ± 118.95 for SNR 1, 2, 3 and 4 (p<0.000). The contrast-to-noise ratio (CNR) was CNR1 78.52 ± 24.11, CNR2 was 76.67 ± 24.38 and CNR3 was 80.54 ± 26.33 in concatenation 6, respectively (p<0.000). The SNR, CNR, and the most efficient concatenation TR value over time are 6, and it is considered to help reduce cross-talk artifact if this is applied to T1 axial images.