• Imaging Science in Dentistry
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• 제34권3호
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• pp.117-122
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• 2004

Purpose : To determine the proper image compression method and ratio without image quality degradation in intraoral digital radiographic images, comparing the discrete cosine transform (DCT)-based JPEG with the wavelet-based JPEG 2000 algorithm. Materials and Methods : Thirty extracted sound teeth and thirty extracted teeth with occlusal caries were used for this study. Twenty plaster blocks were made with three teeth each. They were radiographically exposed using CDR sensors (Schick Inc., Long Island, USA). Digital images were compressed to JPEG format, using Adobe Photoshop v.7.0 and JPEG 2000 format using Jasper program with compression ratios of 5 : 1,9 : 1, 14 : 1,28 : 1 each. To evaluate the lesion detectability, receiver operating characteristic (ROC) analysis was performed by the three oral and maxillofacial radiologists. To evaluate the image quality, all the compressed images were assessed subjectively using 5 grades, in comparison to the original uncompressed images. Results: Compressed images up to compression ratio of 14 : 1 in JPEG and 28 : 1 in JPEG 2000 showed nearly the same the lesion detectability as the original images. In the subjective assessment of image quality, images up to compression ratio of 9 : 1 in JPEG and 14 : 1 in JPEG 2000 showed minute mean paired differences from the original Images. Conclusion : The results showed that the clinically acceptable compression ratios were up to 9 : 1 for JPEG and 14 : 1 for JPEG 2000. The wavelet-based JPEG 2000 is a better compression method, comparing to DCT-based JPEG for intraoral digital radiographic images.

• 한국통신학회논문지
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• 제29권6C호
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• pp.824-833
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• 2004

대역폭과 저장장치의 한계 때문에 의료영상은 전송과 저장 전에 압축되어야 한다. 의료영상 표준인 DICOM(Digital Imaging and Communications in Medicine)은 JPEG 정지영상 압축을 지원하는 구조를 제공한다. 본 논문에서는 의료영상을 JPEG으로 압축하기 위한 방법을 설명하고 JPEG 압축을 위한 두 가지 방법을 제안한다. 첫 번째로 의료영상은 자연영상과는 시각적인 특성이 다르기 때문에, 스펙트럼 분석을 이용한 양자화 테이블을 적응적으로 설계하는 방법을 제안한다. 두 번째로 의료영상은 자연영상과 다르게 픽셀당 비트수가 높기 때문에, 심벌들의 확률분포 특성을 고려한 허프만 테이블을 적응적으로 설계하는 방법을 제안한다. 따라서 본 논문에서는 의료영상에 적합한 양자화 테이블과 허프만 테이블을 설계하는 방법을 제안한다. 실험 결과 JPEG 표준의 양자화 테이블과 수정된 허프만 테이블에 비해 성능이 향상되는 것을 볼 수 있다. 본 논문에서 제안한 방법은 JPEG 표준을 만족하므로, PACS (Picture Archiving and Communications System)에 적용될 수 있다.

• 대한전기학회논문지:시스템및제어부문D
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• 제53권6호
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• pp.453-456
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• 2004

Due to the bandwidth and storage limitations medical images are needed to be compressed before transmission and storage. DICOM (Digital Imaging and Communications in Medicine) specification, which is the medical images standard, provides a mechanism for supporting the use of JPEG still image compression standard. In this paper, we explain a method for compressing medical images by PEG standard and propose two methods for JPEG compression. First, because medical images differ from natural images in optical feature, we propose a method to design adaptively the quantization table using spectrum analysis. Second, because medical images have higher pixel depth than natural images do, we propose a method to design Huffman table which considers the probability distribution feature of symbols. Simulation results show the improved performance compared to the quantization table and the adjusted Huffman table of JPEG standard.

• 대한디지털의료영상학회논문지
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• 제6권1호
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• pp.31-40
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• 2003

Purpose : In a PACS(Picture Archiving Communications System) environment, which is a very important component in a digital medical environment, the compression of digital medical images is a necessary and effective feature. In a current system where JPEG is applied to the compression of medical images, this study is to examine effectiveness and suitability when the JPEG2000, a more advanced compression algorithm for still images, is applied to the compression of medical images. In this thesis, we attempt to address the compressibility for effective clinical usage when compressing medical images, applying the objectivization of clinical evaluation as a function of compressibility. In the experiment al method, the compression was applied at a fixed rate using JPEG2000, and the n the result was compared with compressed images by JPEG. Method : For the performance evaluation, we choose SNR(Signal to Noise Ratio) measurement of an objective evaluation of definition and analyze a subjective evaluation by the ROC(Receiver Operating Characteristic) method. The results of the experiment showed that in the case of JPEG2000 there is hardly any distortion of images, even at high compression ratio(100:1), while regarding noise, the SNR remains around about 40dB, which is also relatively high. Before reading by reference to evaluative materials concerning objective compressed images, it is impossible to apply high compression to images : however, after reading, this can be applied to images that have already existed for some time.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제6권8호
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• pp.1926-1945
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• 2012

In this paper, two practical forensics aided steganalyzers (FA-steganalyzer) for heterogeneous bitmap images are constructed, which can properly handle steganalysis problems for mixed image sources consisting of raw uncompressed images and JPEG decompressed images with different quality factors. The first FA-steganalyzer consists of a JPEG decompressed image identifier followed by two corresponding steganalyzers, one of which is used to deal with uncompressed images and the other is used for mixed JPEG decompressed images with different quality factors. In the second FA-steganalyzer scheme, we further estimate the quality factors for JPEG decompressed images, and then steganalyzers trained on the corresponding quality factors are used. Extensive experimental results show that the proposed two FA-steganalyzers outperform the existing steganalyzer that is trained on a mixed dataset. Additionally, in our proposed FA-steganalyzer scheme, we can select the steganalysis methods specially designed for raw uncompressed images and JPEG decompressed images respectively, which can achieve much more reliable detection accuracy than adopting the identical steganalysis method regardless of the type of cover source.

• 대한의용생체공학회:의공학회지
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• 제15권2호
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• pp.175-182
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• 1994

The international standard for digital compression and coding of continuous-tone still image known as JPEG (Joint Photographic Experts Group) standard is investigated for medical image archiving and communication. The JPEG standard has widely been accepted in the areas of electronic image communication, computer graphics, and multimedia applications, however, due to the lossy character of the JPEG compression its application to the field of medical imaging has been limited. In this paper, the JPEG standard is investigated for medical image compression with a series of head sections of magnetic resonance (MR) images (256 and 4096 graylevels, $256 {\times}256$size). Two types of Huffman codes are employed, i. e., one is optimized to the image statistics to be encoded and the other is a predetermined code, and their coding efficiencies are examined. From experiments, compression ratios of higher than 15 were obtained for the MR images without noticeable distortion. Error signal in the reconstructed images by the JPEG standard appears close to random noise. Compared to existing full-frame bit-allocation technique used for radiological image compression, the JPEG standard achieves higher compression with less Gibb's artifact. Feature of the progressive image build-up of the JPEG progressive coding may be useful in remote diognosis when data is transmitted through slow public communication channel.

• 한국인터넷방송통신학회논문지
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• 제8권1호
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• pp.19-24
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• 2008

본 논문에서는 모바일 영상을 위한 JPEG 양자화 테이블 설계 방법을 제안한다. 작은 화면에서 많은 정보를 표현해야 하는 모바일 영상의 특성을 고려하여 고주파 성분의 영향을 보정하도록 양자화 테이블을 모델링한다. 제안한 모델링 방법을 서비스되는 모바일 QVGA 영상에 적용하여 pre-emphasis factor를 최적화 하였다. 모바일 QVGA 영상에 대한 실험을 통하여 bpp와 PSNR이 향상됨을 보였다.

• 한국정보통신학회논문지
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• 제7권1호
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• pp.142-148
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• 2003

병원에서 발생하는 대용량 의료영상들을 저장 및 전송 할 경우에 저장 매체 수요의 증가와 네트워크 속도의 저하 등의 문제점들이 야기된다. 이러한 문제점들을 해결하기 위한 방안으로 의료영상의 압축이 필요하게 되었다. 본 논문에서는 개개의 여러 압축방법들로 하나의 통합된 환경에서 다양한 종류의 의료영상들을 압축할 수 있는 통합압축환경시스템을 설계 및 구현하였다. 이 통합압축환경에 구현된 압축방법들 중에는 DICOM 3.0규약을 따르는 RLC, 무손실 JPEG 압축방식, JPEG방식들이 있다. 그리고 JPEG2000에 사용되어진 이산웨이블릿변환을 이용한 압축방식과 병 변에 대한 정확성을 높이고 압축률을 좋게 하기 위해서 하나의 영상에서 무손실 압축과 손실 압축을 동시에 하는 방법을 제안하였다. 그리고 영상 정보를 효율적 관리할 수 있게 데이터베이스와 연동이 가능하도록 하였다.

• 한국통신학회논문지
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• 제32권5C호
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• pp.496-512
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• 2007

본 논문에서는 엔트로피 복호화 과정을 부분적으로 수행하여 특징 벡터를 구성하는 새로운 JPEG-2000 압축 영상 검색 시스템을 제안한다. 제안하는 방법은 JPEG-2000 엔트로피 부호화 과정을 통해 발생하는 다양한 문맥 정보를 이용한다. 엔트로피 부호화 기술은 주위 인접한 웨이블릿 계수들의 부호 및 중요 상태 계수의 구조적인 패턴을 분석하여 세 가지의 부호화 패스 및 네 가지의 부호화 기술을 통해 총 19가지의 문맥 정보를 발생한다. 문맥 정보는 산술 부호화 과정에서 부호화 하는 심벌의 확률을 예측하기 위한 모델을 제공한다. 그리고 문맥 정보는 영상의 국부적인 특징을 서술 할 수 있기 때문에 다양한 패턴 특성을 나타내는 질감 영상을 효율적으로 정의할 수 있다. 또한 제안하는 알고리즘은 JPEG-2000 압축 영상에서 복호화 과정을 부분적으로 수행하기 때문에 영상 검색을 수행하기 위한 검색 시간에서 뛰어난 성능을 나타낼 수 있다. 실험을 위해 MIT VisTex 질감 영상을 이용하여 다양한 왜곡 영상 및 유사 영상 데이터베이스를 구성하였으며 기존 검색 알고리즘을 구현하여 제안하는 검색 시스템과 비교 및 평가한다. 본 논문에서 제안하는 알고리즘이 기존 검색 방법보다 검색 성능에서 뛰어날 뿐만 아니라 검색 시간에서도 많은 이득을 얻을 수 있다.

• 대한의용생체공학회:의공학회지
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• 제42권4호
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• pp.167-174
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• 2021

The purpose of this study is to classify TIFF images, PNG images, and JPEG images using deep learning, and to compare the accuracy by verifying the classification performance. The TIFF, PNG, and JPEG images converted from chest X-ray DICOM images were applied to five deep neural network models performed in image recognition and classification to compare classification performance. The data consisted of a total of 4,000 X-ray images, which were converted from DICOM images into 16-bit TIFF images and 8-bit PNG and JPEG images. The learning models are CNN models - VGG16, ResNet50, InceptionV3, DenseNet121, and EfficientNetB0. The accuracy of the five convolutional neural network models of TIFF images is 99.86%, 99.86%, 99.99%, 100%, and 99.89%. The accuracy of PNG images is 99.88%, 100%, 99.97%, 99.87%, and 100%. The accuracy of JPEG images is 100%, 100%, 99.96%, 99.89%, and 100%. Validation of classification performance using test data showed 100% in accuracy, precision, recall and F1 score. Our classification results show that when DICOM images are converted to TIFF, PNG, and JPEG images and learned through preprocessing, the learning works well in all formats. In medical imaging research using deep learning, the classification performance is not affected by converting DICOM images into any format.