• The Korean Journal of Nuclear Medicine Technology
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• v.14 no.1
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• pp.73-77
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• 2010

Purpose: The nuclear medicine technology has been changed. The hard ware is developed so much. Also the soft ware performs a meritorious deed for the development of nuclear medicine technology. We could use the automated region of interest (ROI) instead of manual ROI. We want to know that what difference of quantitative analysis is there between automated ROI and manual ROI Materials and Methods: There are three experimental to make results. The first is what comparing the renal automated ROI and manual ROI. The second is that we compared three threshold ROI that size is difference each others with visible decision. The third is that we compared full, half, quarter automated background, and survey relative function. Results: Although the first has statistically not significant difference, the second and third have significant difference. Threshold, setting smaller threshold then renal outline or bigger, has statistically significant difference (p<0.01). The third is performed with the first experimental. Full background has significant difference, comparing each three type background (p<0.05). Conclusion: The results that there is not significant difference between automated ROI and manual ROI will increase objectivity and operator's convenience. We could know that smaller threshold then renal out line has significant difference in the second experimental. And the third experimental has results because of a increased background nearby live and spleen.

• Journal of Korea Port Economic Association
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• v.22 no.3
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• pp.43-57
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• 2006

Responsiveness to the uncertainty of SCM system shows its competitiveness. In order to secure SCM competitiveness, RFID-related projects aim to enhance both system visibility and process automation. Nowadays, we conduct RFID technology-oriented researches very actively; however, quantitative ROI analysis model from the perspective of SCM does not exist yet, which helps decide the introduction of technology. Therefore, our study suggests a ROI analysis model for the adoption of RFID technology, and we demonstrate its usefulness using the real world data that is taken from one of the government-funded RFID projects in Korea.

• International Journal of Computer Science & Network Security
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• v.23 no.2
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• pp.39-46
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• 2023

The purpose of this study is to introduce a proposed Framework for Evaluating the Return on Investment (ROI) of E-Learning Programs at Saudi Universities. To achieve this goal, the descriptive analysis methodology is used to analyze the literature review about e-learning and its evaluation from different viewpoints, especially from the ROI-related perspective. As well as the literature reviews related to ROI and the methods of calculating it inside society institutes. This study suggests a conceptual framework for evaluating the ROI of E-Learning Programs at Saudi Universities. This framework is based on the merging process among the analyze, design, develop, implement, and evaluate (ADDIE) model for designing e-learning programs, which gives detailed procedures for executing the program, several evaluating models for e-learning, and the Kirkpatrick model for evaluating the ROI of e-learning. It consists of seven stages (analysis, calculating the costs, design, development, implementation, calculation of the benefits, and calculation of the final ROI).

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.94-96
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• 2016

본 논문에서는 기존의 백내장 추출 방법을 개선하기 위해 FCM(Fuzzy C_Means) 알고리즘을 적용하여 백내장을 추출하고 분석하는 방법을 제안한다. 제안된 방법은 애견 안구 영상에서 ROI 영역을 추출한다. 추출된 ROI 영역에서 Fuzzy Stretching 기법을 적용하여 픽셀의 상한 값과 하한 값을 조정한다. 퍼지 스트레칭 기법이 적용된 ROI 영역에 Max-Min 기반 평균 이진화 기법을 적용하여 ROI 영역을 이진화한다. 그리고 퍼지 스트레칭 기법이 적용된 ROI 영역에 FCM 알고리즘을 적용하여 양자화한 후에 양자화된 ROI 영역에서 밝기 평균 이진화 기법을 적용하여 이진화한다. 따라서 Max-Min 기반 이진화 기법을 적용하여 이진화된 ROI 영역과 밝기 평균 이진화 기법을 적용하여 이진화된 ROI 영역을 AND 연산을 적용하여 백내장의 후보 영역을 추출한다. 추출된 백내장의 후보 영역에서 침식, 팽창 기법을 적용하여 ROI 영역의 픽셀 크기를 확대 또는 축소하고 타원 형태를 가진 객체 중에서 ROI의 전체 영역의 크기가 1/5보다 적은 객체를 잡음으로 간주하여 제거한다. 잡음이 제거된 백내장의 후보 영역에서 크기가 3/5이상인 영역을 백내장 영역으로 추출한다. 제안된 방법의 성능을 분석하기 위하여 기존의 백내장 추출 방법과 제안된 백내장 추출 방법을 15개의 백내장 영상을 대상으로 실험한 결과, 제안된 방법이 기존의 백내장 추출 방법보다 백내장 추출률이 개선된 것을 확인하였다.

• Journal of Korea Technology Innovation Society
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• v.7 no.3
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• pp.681-709
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• 2004

The purpose of this study is to classify customers by e-mail responsiveness on time-series analysis and testify the effectiveness of grouping by ROI analysis. Response recency, response frequency and Activity(RFA) of e-mailing systems are adapted for Customer segmentations. ROI analysis are consisted of open, click-through, duration time, personalization, conversion rate and email loyalty index of email systems. Major findings are as follows: RFA analysis is used for customer segmentations that is fundamental process of e-CRM applications. Customers can be grouped into loyal customers, odds customers, dormant customers, secession customers, and observation customers by RFA grouping. Loyal customer group has high point in all ROI index compared to other groups. These results indicated that customer responsiveness of e-mail systems were appropriate methods to group the customer with demographic variables. Therefore, effective e-mail marketing strategy of e-Biz should have suitable active DB and Behavior targeting is best approach to enforce the target e-mail marketing.

• Journal of the Korea Safety Management & Science
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• v.15 no.4
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• pp.427-433
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• 2013

Recently, 18F-FDG PET based CT scan was a critical examination that the after, before plan diagnosis and treatment of tumors. But, due to the distortion of SUV that should be proportional to the metabolic rate of glucose in the tumor, the other measurement methods are being on study. In this study, compared the degree of distortion of SUV that according to the volume of the tumor analysis ROI and VOI using the NEMA IEC Phantom. The results, the SUVmax, mean value are rapidly decreased with threshold value 500 mm2 interval of the ROI analysis, 1500 mm3 interval of the VOI analysis. When compared SUVmax value SUVmean, ROI and VOI analysis VOI measurements was 1.077 times higher SUVmax was 0.981 times highe compared to the value of the ROI measurement. Compare MTV, SUV 2.0 as measured by the volume of the VOI to Volume showed a slightly higher results(Volume / MTV = $93.4 %{\pm}14.8 %$). Considering the above results, Tumor evaluation by 18F-FDG PET / CT scan Consider each threshold value should be analyzed due to larger SUV's Distortion depending on the size of the tumor. VOI analysis is recommended. because it showed the VOI analysis is higher than the ROI analysis SUVmax and lower SUVmean due to VOI analysis than once as a measure of the wider area as measured ROI analysis. MTV (R2 = 0.999), a result close to the actual size of the tumor. but, more research is needed in this regard, because SUV according to the standards of value are affected.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.524-526
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• 2016

본 논문에서는 클러스터의 개수를 다양하게 설정하여 누적된 변화율이 가장 작은 변화율을 가진 클러스터 개수를 동적으로 설정하는 방법을 제안하여 백내장 영역을 추출하는 방법을 제안한다. 제안된 백내장 추출 방법은 애견 안구 영상에서 관심 영역을 ROI 영역으로 설정한다. 설정된 ROI 영역에 퍼지 스트레칭 기법을 적용하여 픽셀의 상한 값과 하한 값을 조정한다. 퍼지 스트레칭 기법이 적용된 ROI 영역에서 FCM 알고리즘을 적용하여 클러스터 소속 행렬의 중심 값의 변화율이 가장 적은 개수를 최적의 클러스터 개수로 설정하여 ROI 영역을 양자화 한다. 양자화된 ROI 영역에서 침식 및 팽창 기법을 적용하고 ROI 영역의 면적을 기준으로 1/5보다 적은 면적을 가진 객체를 잡음으로 간주하여 제거한다. 잡음이 제거된 ROI 영역에서 ROI 면적의 3/5이상인 영역을 가진 안구 객체를 백내장 영역으로 추출한다. 제안된 방법의 효율성을 분석하기 위해서 애견 안구 영상을 대상으로 실험한 결과, 기존의 FCM을 적용하여 ROI 영역을 양자화 하는 처리 시간보다 제안된 클러스터 수 설정 기반 FCM을 적용한 양자화 방법이 처리 시간이 적게 소요되고 객체들을 정확히 분류하는 것을 실험을 통하여 확인하였다.

• 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.

• Proceedings of the Korea Information Processing Society Conference
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• 2007.11a
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• pp.387-390
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• 2007

CMMI 도입은, 추진 이후 프로세스가 조직 내에 내재화되기까지, 상당한 기간과 인력이 소요되는 고비용의 조직 활동이다. 따라서 CMMI 도입 결정에 앞서 ROI 분석을 통한 정확한 의사결정이 필요하다. 지금까지의 CMMI 도입과 관련한 ROI산정방식은, CMMI 의 효과산정방식이 구체적이지 않고, 비용측면에 있어서도 프로세스 정의 및 수행비용을 중심으로 산정할 뿐, 지속적인 내재화에 소요되는 비용을 간과하고 있어, 의사결정지표로서 정확성이 결여되었다. 이에 본 논문은 수익성 개선과 생산성 개선에 기반을 두어 효과를 산정하고, 비용 산정은 프로세스 내재화에 따른 지속적 관리활동이라는 특징을 반영하여, 보다 실제적이고 정확한 ROI 분석 방법인 iROI 산정방법을 제안한다. 증명이 어려운 CMMI 투자성과 분석에 활용될 수 있으며, 타 투자분야와 비교할 수 있는 공정한 의사결정 판단근거로서 활용될 수 있다.

• Journal of Auto-vehicle Safety Association
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• v.14 no.2
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• pp.20-25
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• 2022

This paper represents an adaptive Region of Interest (ROI) decision for real-time performance in an autonomous driving perception module. Since the whole automated driving system consists of numerous modules and subdivisions of module occur, it is necessary to consider the characteristics, complexity, and limitations of each module. Furthermore, Light Detection And Ranging (Lidar) sensors require a considerable amount of time. In view of these limitations, division of submodule is inevitable to represent high real-time performance for stable system. This paper proposes ROI to reduce the number of data respect to computation time. ROI is set by a road's design speed and the corresponding ROI is applied differently to each vehicle considering its speed. The simulation model is constructed by ROS, and overall data analysis is conducted by Matlab. The algorithm is validated using real-time driving data in urban environment, and the result shows that ROI provides low computational costs.