• Journal of the Institute of Convergence Signal Processing
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• v.3 no.1
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• pp.33-40
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• 2002

In this paper, we proposed a lossless compression algorithm of medical images which is essential technique in picture archive and communication system. Mammographic image and magnetic resonance image in among medical images used in this study, proposed a region growing segmentation algorithm for compression of these images. A proposed algorithm was partition by three sub region which error image, discontinuity index map, high order bit data from original image. And generated discontinuity index image data and error image which apply to a region growing algorithm are compressed using JBIG(Joint Bi-level Image experts Group) algorithm that is international hi-level image compression standard and proper image compression technique of gray code digital Images. The proposed lossless compression method resulted in, on the average, lossless compression to about 73.14% with a database of high-resolution digital mammography images. In comparison with direct coding by JBIG, JPEG, and Lempel-Ziv coding methods, the proposed method performed better by 3.7%, 7.9% and 23.6% on the database used.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.443-445
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• 2021

갑상선 결절(thyroid nodule)은 검진 인구에서 빈번하게 진단되는 질환이지만 현재까지 진단방법은 경험적이며 정성적 판단에 의존하고 있는 실정이다. 본 연구는 갑상선 결절을 평가하기 위하여 시행한 초음파 의료영상을 이용하여 정량 분석할 수 있는 소프트웨어를 개발하였으며 갑상선 양성 결절환자에서의 임상활용 가능성을 평가하고자 한다. 임상 연구는 총 13명의 갑상선 양성 결절 환자를 대상으로 하였다. 환자별 갑상선 초음파영상을 이용하여 정상부위와 병변부위에서 정량 지표인 변동계수를 각각 측정하였다. 환자별 정상부위와 병변부위의 변동계수 차이는 대응표본 T 검정을 사용하여 비교하였으며 유의한 차이를 확인할 수 있었다. 본 연구를 통하여 개발한 정량분석 소프트웨어를 실제 갑상선 양성 결절 환자에서 갑상선 결절을 분석·평가하는데 활용할 수 있을 것으로 판단된다.

• Journal of the Korea Computer Graphics Society
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• v.28 no.2
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• pp.11-19
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• 2022

Recently, various researches on medical image generation have been suggested, and it becomes crucial to accurately evaluate the quality and diversity of the generated medical images. For this purpose, the expert's visual turing test, feature distribution visualization, and quantitative evaluation through IS and FID are evaluated. However, there are few methods for quantitatively evaluating medical images in terms of fidelity and diversity. In this paper, images are generated by learning a chest CT dataset of non-small cell lung cancer patients through DCGAN and PGGAN generative models, and the performance of the two generative models are evaluated in terms of fidelity and diversity. The performance is quantitatively evaluated through IS and FID, which are one-dimensional score-based evaluation methods, and Precision and Recall, Improved Precision and Recall, which are two-dimensional score-based evaluation methods, and the characteristics and limitations of each evaluation method are also analyzed in medical imaging.

• KIPS Transactions on Computer and Communication Systems
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• v.12 no.3
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• pp.119-126
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• 2023

Sarcopenia is not well known enough to be classified as a disease in 2021 in Korea, but it is recognized as a social problem in developed countries that have entered an aging society. The diagnosis of sarcopenia follows the international standard guidelines presented by the European Working Group for Sarcopenia in Older People (EWGSOP) and the d Asian Working Group for Sarcopenia (AWGS). Recently, it is recommended to evaluate muscle function by using physical performance evaluation, walking speed measurement, and standing test in addition to absolute muscle mass as a diagnostic method. As a representative method for measuring muscle mass, the body composition analysis method using DEXA has been formally implemented in clinical practice. In addition, various studies for measuring muscle mass using abdominal images of MRI or CT are being actively conducted. In this paper, we develop an AI image segmentation model based on abdominal images of CT with a relatively short imaging time for the diagnosis of sarcopenia and describe the multicenter validation. We developed an artificial intelligence model using U-Net that can automatically segment muscle, subcutaneous fat, and visceral fat by selecting the L3 region from the CT image. Also, to evaluate the performance of the model, internal verification was performed by calculating the intersection over union (IOU) of the partitioned area, and the results of external verification using data from other hospitals are shown. Based on the verification results, we tried to review and supplement the problems and solutions.

• The Korean Journal of Applied Statistics
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• v.24 no.4
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• pp.695-708
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• 2011

Image fusion is the process of combining multiple images of the same scene into a single fused image with application to many fields, such as remote sensing, computer vision, robotics, medical imaging and military affairs. The widely used image fusion rules that use wavelet transform have been based on a simple comparison with the activity measures of local windows such as mean and standard deviation. In this case, information features from the original images are excluded in the fusion image and distorted fusion images are obtained for noisy images. In this paper, we propose the use of a nonparametric squared ranks test on the quality of variance for two samples in order to overcome the influence of the noise and guarantee the homogeneity of the fused image. We evaluate the method both quantitatively and qualitatively for image fusion as well as compare it to some existing fusion methods. Experimental results indicate that the proposed method is effective and provides satisfactory fusion results.

• Proceedings of the Korea Information Processing Society Conference
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• 2009.11a
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• pp.209-210
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• 2009

임상공학 분야에서의 두 가지 트랜드의 변화가 주목된다. 급성질환에서 만성질환으로 의료/건강관리의 중요성이 증가하는 것이고, 이를 다루는 의료정보의 처리기기가 컴퓨터화 되고 있으며, 통합되어 이용되어 가는 것이다. 또한, 이 두 가지의 변화가 융합되는 방향으로 임상공학 분야의 연구의 중요성이 증가하고 있다. 이와 같은 의료정보처리의 요구사항을 만족하기 위하여 기 연구한 의료영상기반 협업 플랫폼 PACS-Grid[1]을 바탕으로 만성질환 지향형 의료정보 통합 가시화의 통합방법을 다룬다. 더불어 이를 지원하기 위한 서비스 지향형 PACS-Grid 플랫폼을 제안한다.

• 한국과학기술학회:학술대회논문집
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• 2008.12a
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• pp.85-93
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• 2008

• Journal of the Korea Computer Graphics Society
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• v.11 no.2
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• pp.26-33
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• 2005

의료기기들 중 기능 영상을 보기 위해 이용되는 PET 장치에서 획득된 결과 영상은 선명하지 않기 때문에, 해부학적 구조와 기능 영상을 동시에 보기 위해서는 선명한 영상을 제공하는 CT 와 PET 장치와 하나로 통합하여 영상을 획득하게 되었다. 그래서 한번의 촬영으로 PET/CT 영상을 얻을 수 있게 된 것이다. 서로 다른 특성을 갖는 이미지를 융합하게 되면 보다 정확한 진단을 내리는데 많은 도움을 준다. 본 논문은 CT 영상에서 폐 영역을 반 자동(Semi-Auto)으로 분리한 후 PET 영상에 자동으로 융합하는 방법을 제안한다. 반 자동 폐 영역 분할을 위해 1 차원 신호 처리 기법과 Seeded Region Growing 기법을 사용한다. 수행된 폐 분리 결과는 몸의 해부학적 구조를 보기 위해 사용되는 CT 영상에서 추출한 폐 영역을 기능을 보기 위한 PET 영상에 퓨전 함으로서 진단 전문가가 보다 정확한 진단을 하는데 도움이 될 것이다. 또한 이러한 기능을 쉽게 구현하고 사용할 수 있도록 시각 프로그래밍 기법을 접목하였다.

• Journal of the Korean Society of Radiology
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• v.81 no.6
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• pp.1290-1304
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• 2020

Medical image analyses have been widely used to differentiate normal and abnormal cases, detect lesions, segment organs, etc. Recently, owing to many breakthroughs in artificial intelligence techniques, medical image analyses based on deep learning have been actively studied. However, sufficient medical data are difficult to obtain, and data imbalance between classes hinder the improvement of deep learning performance. To resolve these issues, various studies have been performed, and data augmentation has been found to be a solution. In this review, we introduce data augmentation techniques, including image processing, such as rotation, shift, and intensity variation methods, generative adversarial network-based method, and image property mixing methods. Subsequently, we examine various deep learning studies based on data augmentation techniques. Finally, we discuss the necessity and future directions of data augmentation.

• Journal of the Korea Convergence Society
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• v.10 no.11
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• pp.173-179
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• 2019

DICOM(Digital Imaging and Communications in Medicine) imaging plays a significant role in the diagnosis and treatment of the human body, and design modeling is a technology of planning shapes in three dimensions according to the purpose. In this study, we converge these two technologies to observe the relationships of the cross-section, volume, and surface area to the morphological changes of the external ear canal. The experiment applied medical imaging technologies to acquire sections of the human body to create and divide centerlines using 3D shapes extracted from 19 external ear canals by applying stereolithography and 3-matic program. The results showed that the cross-sectional structure of the external ear canal had various shapes, such as oval (38.5%), semicircular (28.2%), mixed (17.9%), square (10.2%), and wrinkled (5.1%). In addition, the cross-sectional area of each phase increased as the length of the external ear canal increased, and the volume and surface area decreased towards the direction of the eardrum. However, the surface area reduction rate was relatively low. This indicates that the structure becomes irregular towards the direction of the eardrum.