• 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.

• 대한의용생체공학회:의공학회지
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• 제10권3호
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• pp.243-252
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• 1989

In this study, medical images, which are X-ray image and CT image, are compressed by the adam live coding technique. The medical images may be treated as special ones, because they are different from general images in many respects. The statistical characteristics that medical images only have in transform domain are analyzed, and then the improved quantization method is proposed for medical images. For chest X-ray image and CT head image, the better results are obtained by the improved adaptive coding technique.

• 인터넷정보학회논문지
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• 제17권3호
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• pp.45-54
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• 2016

의료 이미지는 사람 신체의 비정상적인 상태를 발견하는데 효과적인 자료로 사용되고 있다. 일반적으로 환자는 다양한 이유로 다른 종류의 의료 기관을 방문하고, 심각한 질병 특징을 가지는 의료 이미지에 대해 2차 소견을 얻기를 원한다. 현재에는 개인의 의료 이미지가 여러 의료 기관에 산재되어 있기 때문에, 2차 소견을 얻을 때 자신과 관련된 모든 정보를 직접 가지고 다른 의료진을 찾아가야 하는 불편함이 있다. 이런 두 가지 동기로 인해, 본 논문에서는 의료 이미지 보관 및 판독 서비스를 제안하고자 한다. 그러므로, 의료 이미지 보관 및 판독 서비스의 설계 모델 및 구현 결과를 본 논문에서 제시하고, 저비용 개인 헬스케어 서비스로서의 실용적 가치를 증명하고자 한다. 환자는 제안하는 서비스를 사용함으로써 언제든 자신의 의료 이미지 정보를 확인할 수 있고 의료진을 찾아갈 필요 없이 간편하게 의료 이미지 분석을 할 수 있다.

• Journal of Information Processing Systems
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• 제12권1호
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• pp.129-148
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• 2016

Acute myocardial infarction (AMI) is one of the three emergency diseases that require urgent diagnosis and treatment in the golden hour. It is important to identify the status of the coronary artery in AMI due to the nature of disease. Therefore, multi-modal medical images, which can effectively show the status of the coronary artery, have been widely used to diagnose AMI. However, the legacy system has provided multi-modal medical images with flat and unstructured data. It has a lack of semantic information between multi-modal images, which are distributed and stored individually. If we can see the status of the coronary artery all at once by integrating the core information extracted from multi-modal medical images, the time for diagnosis and treatment will be reduced. In this paper, we analyze semantic relations between multi-modal medical images based on coronary anatomy for AMI. First, we selected a coronary arteriogram, coronary angiography, and echocardiography as the representative medical images for AMI and extracted semantic features from them, respectively. We then analyzed the semantic relations between them and defined the convergence data model for AMI. As a result, we show that the data model can present core information from multi-modal medical images and enable to diagnose through the united view of AMI intuitively.

• 대한의용생체공학회:의공학회지
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• 제15권1호
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• pp.57-62
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• 1994

The demand of image compression is increasing now for the integration of medical images into the hospital information system. Even though the quantitative distortion can be measured from the difference between original and reconstructed images, it doesn't include the nonlinear characteristics of human visual system. In this study, we have evaluated the nonlinear characteristics of human visual system and applied them to the compression of medical images. The distortion measures which reflect the characteristics of human visual system has been considered. This image compression procedure consists of coding scheme using JND (Just Noticeable Difference) curve, polynomial approximation and BTC (Block Truncation Coding). Results show that this method can be applied to CT images, scanned film images and other kinds of medical images with the compression ratio of 5-10:1 without any noticeable distortion.

• 대한디지털의료영상학회논문지
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• 제16권1호
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• pp.1-5
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• 2014

This study is about efficiency export and import of medical images According to the Type of Compression. PACS to be used in many hospitals and medical images export is growing more and more because cheaper and good usability than film system. Thereby export department takes a lot of time, which may cause the patient discomfort. Compression images takes less time for images export, import than nocompression images. therefore, if no significant problems clinicians to view the images, this is one method to compressed images export for reduce the time and it will provide less cost and shorter time for patient.

• 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.

• 대한두경부종양학회지
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• 제26권2호
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• pp.198-203
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• 2010

Objectives : Getting full information from axial CT images needs experiences and knowledge. Sagittal and coronal images could give more information but we have to draw 3-dimensional images in mind with above informations. With aid of 3D reconstruction softwares, CT data are converted to visible 3D images. We tried to compare medical photographs of 15 surgical specimens from neck tumors with 3D reconstructed images of same patients. Material and Methods : Fifteen patients with neck tumors treated surgically were recruited. Medical photograph of the surgical specimens were collected for comparison. 3D reconstruction of neck CT from same patients with aid of 3D-doctor software gave 3D images of neck masses. Width and height of tumors of each photos and images from the same cases were calculated and compared statistically. Visual similarities were rated between photos and 3D images. Results : No statatistical difference were found in size between medical photos and 3D images. Visual similarity score were higher between 2 groups of images. Conclusion : 3D reconstructed images of neck mass looked alike the real photographs of excised neck mass with similar calculated sizes. It could give us reliable visual information about the mass.

• ETRI Journal
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• 제39권2호
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• pp.173-180
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• 2017

Multimodal medical image fusion is the process of retrieving valuable information from medical images. The primary goal of medical image fusion is to combine several images obtained from various sources into a distinct image suitable for improved diagnosis. Complexity in medical images is higher, and many soft computing methods are applied by researchers to process them. Intuitionistic fuzzy sets are more appropriate for medical images because the images have many uncertainties. In this paper, a new method, based on Sugeno's intuitionistic fuzzy set (SIFS), is proposed. First, medical images are converted into Sugeno's intuitionistic fuzzy image (SIFI). An exponential intuitionistic fuzzy entropy calculates the optimum values of membership, non-membership, and hesitation degree functions. Then, the two SIFIs are disintegrated into image blocks for calculating the count of blackness and whiteness of the blocks. Finally, the fused image is rebuilt from the recombination of SIFI image blocks. The efficiency of the use of SIFS in multimodal medical image fusion is demonstrated on several pairs of images and the results are compared with existing studies in recent literature.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제7권1호
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• pp.132-148
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• 2013

Via the Internet, the information infrastructure of modern health care has already established medical information systems to share electronic health records among patients and health care providers. Data hiding plays an important role to protect medical images. Because modern medical devices have improved, high resolutions of medical images are provided to detect early diseases. The high quality medical images are used to recognize complicated anatomical structures such as soft tissues, muscles, and internal organs to support diagnosis of diseases. For instance, 16-bit depth medical images will provide 65,536 discrete levels to show more details of anatomical structures. In general, the feature of low utilization rate of intensity in 16-bit depth will be utilized to handle overflow/underflow problem. Nowadays, most of data hiding algorithms are still experimenting on 8-bit depth medical images. We proposed a novel reversible data hiding scheme testing on 16-bit depth CT and MRI medical image. And the peak point and zero point of a histogram are applied to embed secret message k bits without salt-and-pepper.