• Imaging Science in Dentistry
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• v.34 no.3
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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.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.6C
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• pp.824-833
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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 JPEG 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. Therefore, we propose methods to design a quantization table and Huffman table suitable for medical images. Simulation results show the improved performance compared to the quantization table and the adjusted Huffman table of JPEG standard. Proposed methods which are satisfied JPEG Standard, can be applied to PACS (Picture Archiving and Communications System).

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.6
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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.

• Korean Journal of Digital Imaging in Medicine
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• v.6 no.1
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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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• v.6 no.8
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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.

• Journal of Biomedical Engineering Research
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• v.15 no.2
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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.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.8 no.1
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• pp.19-24
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• 2008

This paper presents a new JPEG quantization table design method for mobile images in handset. From the characteristics of the mobile images, we propose a modeling method of the quantization table and select the optimized pre-emphasis factor from various sets of $240{\times}320$ images. From the experiment, we show that the performance is improved in the sense of bpp and PSNR, applying the proposed method.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.1
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• pp.142-148
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• 2003

We compress medical images in order to solve problems both of request of storage mediums and of a low network speed. In this paper, integration compression environment has been developed for unity of various compression methods. Various compression methods that are implemented by integration compression environment, RLC, Lossless JPEG, and JPEG, comply with the DICOM 3.0. A compression method using DWT is implemented at it. And a unit method of Lossless compression method and lossy compression method is designed to improve images quality and to progress compression ratio. Diverse medical images can be compressed by each compression method. And integration compression environment is operated together database so that information of medical images is administered.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.5C
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• pp.496-512
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• 2007

In this paper, we propose a novel JPEG-2000 compressed image retrieval system using feature vector extracted through partial entropy decoding. Main idea of the proposed method is to utilize the context information that is generated during entropy encoding/decoding. In the framework of JPEG-2000, the context of a current coefficient is determined depending on the pattern of the significance and/or the sign of its neighbors in three bit-plane coding passes and four coding modes. The contexts provide a model for estimating the probability of each symbol to be coded. And they can efficiently describe texture images which have different pattern because they represent the local property of images. In addition, our system can directly search the images in the JPEG-2000 compressed domain without full decompression. Therefore, our proposed scheme can accelerate the work of retrieving images. We create various distortion and similarity image databases using MIT VisTex texture images for simulation. we evaluate the proposed algorithm comparing with the previous ones. Through simulations, we demonstrate that our method achieves good performance in terms of the retrieval accuracy as well as the computational complexity.

• Journal of Biomedical Engineering Research
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• v.42 no.4
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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.