• KIPS Transactions on Computer and Communication Systems
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• v.11 no.7
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• pp.233-240
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• 2022

Most of the recent AI researches has focused on developing AI models. However, recently, artificial intelligence research has gradually changed from model-centric to data-centric, and the importance of learning data is getting a lot of attention based on this trend. However, it takes a lot of time and effort because the preparation of learning data takes up a significant part of the entire process, and the generation of labeling data also differs depending on the purpose of development. Therefore, it is need to develop a tool with various labeling functions to solve the existing unmetneeds. In this paper, we describe a labeling system for creating precise and fast labeling data of medical images. To implement this, a semi-automatic method using Back Projection, Grabcut techniques and an automatic method predicted through a machine learning model were implemented. We not only showed the advantage of running time for the generation of labeling data of the proposed system, but also showed superiority through comparative evaluation of accuracy. In addition, by analyzing the image data set of about 1,000 patients, meaningful diagnostic indexes were presented for men and women in the diagnosis of sarcopenia.

• The Journal of the Korea Contents Association
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• v.14 no.3
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• pp.11-21
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• 2014

Multi-scale image segmentation is important in many applications such as image stylization and medical diagnosis. This paper proposes a novel segmentation algorithm based on MSER(maximally stable extremal region) which captures multi-scale structure and is stable and efficient. The algorithm collects MSERs and then partitions the image plane by redrawing MSERs in specific order. To denoise and smooth the region boundaries, hierarchical morphological operations are developed. To illustrate effectiveness of the algorithm's multi-scale structure, effects of various types of LOD control are shown for image stylization. The proposed technique achieves this without time-consuming multi-level Gaussian smoothing. The comparisons of segmentation quality and timing efficiency with mean shift-based Edison system are presented.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.36C no.12
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• pp.90-97
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• 1999

Recently, there have been many researches to automate processing and analysing image data in medical field, due to the advance of image processing techniques, the fast communication network and high performance hardware. In this paper, we design and implement the system based on the multi-layer neural network model to be able to analyze, differentiate and count blood cells in the peripheral blood image. To do these, we segment red and white-blood cell in blood image acquired from microscope with CCD(Charge-coupled device) camera and then apply the various feature extraction algorithms to classify. In addition to, we reduce multi-variate feature number using PCA(Principle Component Analysis) to construct more efficient classifier. So, in this paper, we are sure that the proposed system can be applied to a pathological guided system.

• Journal of Broadcast Engineering
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• v.25 no.1
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• pp.13-24
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• 2020

There are about nine million power line poles and 1.3 million kilometers of the power line for electric power distribution in Korea. Maintenance of such a large number of electric power facilities requires a lot of manpower and time. Recently, various fault diagnosis techniques using artificial intelligence have been studied. Therefore, in this paper, proposes a power line deflection detect system using artificial intelligence and computer vision technology in images taken by vision system. The proposed system proceeds as follows. (i) Detection of transmission tower using object detection system (ii) Histogram equalization technique to solve the degradation in image quality problem of video data (iii) In general, since the distance between two transmission towers is long, a panoramic video stitching process is performed to grasp the entire power line (iv) Detecting deflection using computer vision technology after applying power line detection algorithm This paper explain and experiment about each process.

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.10
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• pp.285-290
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• 2021

In the medical field, disease diagnosis and prediction research using artificial intelligence technology is being actively conducted. It is being released as a variety of products for disease diagnosis and prediction, which are most widely used in the application of artificial intelligence technology based on medical images. Artificial intelligence is being applied to diagnose diseases, to classify diseases into benign and malignant, and to separate disease regions for use in identification or reading according to the risk of disease. Recently, in connection with cloud technology, its utility as a service product is increasing. Among the diseases dealt with in this paper, liver disease is a disease with very high risk because it is difficult to diagnose early due to the lack of pain. Artificial intelligence technology was introduced based on medical images as a non-invasive diagnostic method for diagnosing these diseases. We describe the development of a web service to help the most meaningful clinical reading of liver cirrhosis patients. Then, it shows the web service process and shows the operation screen of each process and the final result screen. It is expected that the proposed service will be able to diagnose liver cirrhosis at an early stage and help patients recover through rapid treatment.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.11a
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• pp.1071-1074
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• 2011

최근 스마트기기의 보급 증가로 모바일 PACS 시장 활성화가 기대되고 있으며, 의료영상에 대한 확인 및 확대/축소 기능에 진단 및 처방 기능까지 포함하는 다양한 형태의 서비스 확대가 예상된다. 본 논문에서는 고용량의 DICOM 이미지를 스마트기기에 효율적인 전송하기 위한 PACS 게이트웨이 시스템 개발의 중간 단계로서 설계 결과를 제시한다.

• The Journal of the Acoustical Society of Korea
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• v.17 no.6
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• pp.79-84
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• 1998

초음파 진단장치는 X선 촬영과 달리 인체에 해를 주지 않고 계속적으로 장시간 관 찰할 수 있고 실시간으로 영상을 볼 수 있으며, 또 타장비에 비해 가격이 저렴하고 소형이 라는 장점이 있다. 현재는 이 영상들을 대용량 저장매체에 저장되어 컴퓨터를 통해 재생하 여 볼 수 있게 되었다. 본 논문에서는 이러한 막대한 양의 영상데이터를 검색하기 위한 새 로운 검색방법을 제시한다. 제안하는 검색방법은 검색을 원하는 정지화상의 컬러이미지를 제시하면 시스템이 이를 자동으로 분석하여 이미지 데이터베이스에 저장된 유사한 이미지데 이터들과 관련된 정보들을 질의결과로 나타내어 쉽게 검색하고자 한다. 이를 위하여 사용자 가 제시한 정지화상을 Bitmap으로 구성하고, Bitmap전체의 비디오 메모리에서 검색할 부분 영역을 검색대상으로 설정한다. 이 값을 key값으로 우선적으로 여기에 원하는 유사비를 설 정한 후 전체 동화상의 각 프레임에서 추출한 비디오 메모리 데이터와 검색 화면의 비디오 메모리를 Pixel별로 비교하여, 사용자가 원하는 영상데이터의 위치point 값과 유사비율값을 보관한다. point값으로 보관된 것을 유사비율에 따라 우선 순위를 정하여 데이터베이스에 보 관하고 이 보관된 후보 이미지들을 순위별로 화면에 나타내어 사용자가 원하는 이미지데이 터를 쉽고 빠르게 검색할 수 있었다.

• Proceedings of the Korean Information Science Society Conference
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• 2008.06c
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• pp.545-548
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• 2008

건강과 미용에 대한 관심은 IT 기술의 발전과 결합하여 유-헬스케어 산업의 부흥을 이끌고 있다. 특히, 향상된 디지털 영상 장비를 통한 각종 신체 정보 분석이 가능해짐에 따라 피부 자가 진단 시스템 등에 대한 연구 및 제품 출시가 활발히 이루어지고 있다. 본 논문에서는 이진 영상의 각 축에 대한 밀도 분석을 통해 특징점을 검출하는 방법을 소개하고 이를 토대로 자외선 광으로 촬영한 피부 현미경 영상에서 피지 부위를 자동 검출하는 기법을 제안하며 실험을 통하여 성능을 분석한다.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.10
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• pp.239-246
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• 2020

In the medical field where artificial intelligence technology is introduced, research related to clinical decision support system(CDSS) in relation to diagnosis and prediction is actively being conducted. In particular, medical imaging-based disease diagnosis area applied AI technologies at various products. However, medical imaging data consists of inconsistent data, and it is a reality that it takes considerable time to prepare and use it for research. This paper describes a one-stop AI learning platform for converting to medical image standard R_CDM(Radiology Common Data Model) and supporting AI algorithm development research based on the dataset. To this, the focus is on linking with the existing CDM(common data model) and model the system, including the schema of the medical imaging standard model and report information for multi-center research based on DICOM(Digital Imaging and Communications in Medicine) tag information. And also, we show the execution results based on generated datasets through the AI learning platform. As a proposed platform, it is expected to be used for various image-based artificial intelligence researches.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.1900-1901
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• 2007

한의학에서 혀는 인체 내 생리, 병리적 변화를 반영하여 진단에 있어 중요한 지표자료로 사용하고 있다. 하지만 설진은 진단환경에 의한 영향을 많이 받게 되어 객관적이고 정량화된 결과를 얻기 힘들며, 그로 인해 한의사의 진단에 대한 신뢰성도 낮아 널리 활용되지 못하고 있다. 이를 해결하기 위해 본 연구에서는 제한된 측정환경을 제공하는 디지털 설진 시스템을 통해 한의사로부터 황태라 진단받은 48명과 백태라 진단받은 14명의 설 영상을 획득하고, 설태의 컬러벡터값을 구하여 백태와 황태를 구분하는데 유의한 변수를 도출하였으며, 이를 이용하여 한의사의 설태 진단과 일치율이 높은 판별함수를 도출하였다. 이와같은 연구방법은 백태, 황태 뿐 아니라 회태나 흑태 또는 후태나 박태를 구분하는 데에도 유용하게 쓰일 수 있을 것이며 앞으로 한의사의 설진과정을 객관화, 과학화하는 과정에 기여할 수 있을 것이다.