• Journal of the Korea Institute of Information and Communication Engineering
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• v.6 no.7
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• pp.1084-1088
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• 2002

Currently, the lack of equipments for the medical practice and education made it impossible for the people in medical institution to carry out suitable experiments for observing human bodies .In this paper, the authors embodied three dimensional images and moving pictures for the human skeletal structure, digestive organs, cardiovascular system and their processes over the internet framework. The three dimensional images and moving picture made it possible for the general people as well as the specialists to observe and obtain informations with regard to the human body. Especially, the authors realized a framework for visualizing the human bodies in three dimensional images, via which a detailed and realistic architecture for the human body and its organs can be obtained. The system developed in this paper can be used in the practice and education of the people engaged in medical fields.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.6 no.2
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• pp.264-270
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• 2002

Current]y, the lack of equipments for the medical practice and education made it impossible for the people in medical institution to carry out suitable experiments for observing human bodies. In this paper, the authors embodied three dimensional images and moving pictures for the human skeletal structure, digestive organs and their processes over the internet framework. The three dimensional images and moving picture made it possible for the general people as well as the specialists to observe and obtain informations with regard to the human body. Especially, the authors realized a framework for visualizing the human bodies in three dimensional images, via which a detailed and realistic architecture for the human body and its organs can be obtained. The system developed in this Paper can be used in the practice and education of the people engaged in medical fields.

• Proceedings of the KOSOMBE Conference
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• v.1997 no.11
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• pp.541-545
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• 1997

This paper presents a modified geometric active contour model or edge detection and segmentation of computed tomography(CT) scan images. The method is based on the level setup approach developed by Osher and Sethian and the modeling of propagation fronts with curvature dependent speeds by Malladi. Based on above algorithms, the geometric active contour is obtained through a particular level set of hypersurface lowing along its gradient force and curvature force. This technique retains the attractive feature which is topological and geometric flexibility of the contour in recovering objects with complex shapes and unknown topologies. But there are limitations in this algorithm which are being not able to separate the object with weak difference from neighbor object. So we use speed limitation filter to overcome those problems. We apply a 2D model to various synthetic cases and the three cases of real CT scan images in order to segment objects with complicated shapes and topologies. From the results, the presented model confirms that it attracts very naturally and efficiently to the desired feature of CT scan images.

• Journal of Biomedical Engineering Research
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• v.18 no.4
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• pp.349-356
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• 1997

In this paper, we propose the lossless coding: scheme for progressive transmission of medical images. The input image is decomposed by the proposed fast adaptive subband decomposition method which is suited for a lossless coding. The decomposed images are coded by an arithmetic coder with two conditioning pixels, and the conditioning pixels are selected differently according to the property of the subbands. The conditioning contexts are usually quantized to reduce the conditional state, and the optimization method of quantization is proposed For the purpose of improving compression ratio in this paper. The proposed lossless coding scheme provides the asymmetric structure of cosec and results in better compression ability than the JPEC lossless coding[ 1 ].

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2002.11a
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• pp.352-355
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• 2002

Currently, the lack of equipments for the medical practice and education made it impossible for the people in medical institution to carry out suitable experiments for observing human bodies. In this paper, the authors embodied three dimensional images and moving pictures for the human skeletal structure, digestive organs, cardiovascular system and their processes over the internet framework. The three dimensional images and moving picture made it possible for the general people as well as the specialists to observe and obtain informations with regard to the human body. Especially, the authors realized a framework for visualizing the human bodies in three dimensional images, via which a detailed and realistic architecture for the human body and its organs can be obtained. The system developed in this paper can be used in the practice and education of the people engaged in medical fields.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.18
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• pp.8625-8628
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• 2016

Background: Central lymph node metastasis(CLNM) is common in papillary thyroid microcarcinoma (PTMC). The aim of this study was to define the pathohistologic risk grading based on surgical outcomes. Materials and Methods: Statistical analysis was performed to figure out the optimal cut-off values of size in preoperative ultrasound images for defining the risk of CLNM in papillary thyroid microcarcinoma. Receiver operating characteristic curves (ROC) studies were carried out to determine the cutoff value(s) for the predictor(s). All the patients were divided into two groups according to the above size and the clinic-pathological and immunohistochemical parameters were compared to determine the significance of findings. Results: The optimal cut-off value of tumor size to predict the risk of CLNM in papillary thyroid microcarcinoma was 0.575 cm (area under the curve 0.721) according to the ROC curves. Significant differences were observed on the multifocality, extrathyroidal extension and central lymph node metastasis between two groups which were divided according to the tumor size by the cutoff values. Patients in two groups showed different positive rate and intensity of Ki67. Conclusions: The size of PTMC in ultrasound images are helpful to predict the aggressiveness of the tumors, it could be an easy predictor for PTMC prognosis and assist us to choose treatment.

• Nuclear Engineering and Technology
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• v.53 no.2
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• pp.626-636
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• 2021

Boron-neutron capture therapy (BNCT) is a cancer treatment method that exploits the high neutron reactivity of boron. Monitoring the prompt gamma rays (PGs) produced during neutron irradiation is essential for ensuring the accuracy and safety of BNCT. We investigate the imaging of PGs produced by the boron-neutron capture reaction through Monte Carlo simulations of a gamma camera with a SrI₂ scintillator and parallel-hole collimator. GAGG scintillator is also used for a comparison. The simulations allow the shapes of the energy spectra, which exhibit a peak at 478 keV, to be determined along with the PG images from a boron-water phantom. It is found that increasing the size of the water phantom results in a greater number of image counts and lower contrast. Additionally, a higher septal penetration ratio results in poorer image quality, and a SrI₂ scintillator results in higher image contrast. Thus, we can simulate the BNCT process and obtain an energy spectrum with a reasonable shape, as well as suitable PG images. Both GAGG and SrI₂ crystals are suitable for PG imaging during BNCT. However, for higher imaging quality, SrI₂ and a collimator with a lower septal penetration ratio should be utilized.

• Journal of Biomedical Engineering Research
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• v.44 no.5
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• pp.303-314
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• 2023

This study aimed to assess the practical feasibility of advanced deformable image registration (DIR) algorithms in radiotherapy by employing two distinct datasets. The first dataset included 14 4D lung CT scans and 31 head and neck CT scans. In the 4D lung CT dataset, we employed the DIR algorithm to register organs at risk and tumors based on respiratory phases. The second dataset comprised pre-, mid-, and post-treatment CT images of the head and neck region, along with organ at risk and tumor delineations. These images underwent registration using the DIR algorithm, and Dice similarity coefficients (DSCs) were compared. In the 4D lung CT dataset, registration accuracy was evaluated for the spinal cord, lung, lung nodules, esophagus, and tumors. The average DSCs for the non-learning-based SyN and NiftyReg algorithms were 0.92±0.07 and 0.88±0.09, respectively. Deep learning methods, namely Voxelmorph, Cyclemorph, and Transmorph, achieved average DSCs of 0.90±0.07, 0.91±0.04, and 0.89±0.05, respectively. For the head and neck CT dataset, the average DSCs for SyN and NiftyReg were 0.82±0.04 and 0.79±0.05, respectively, while Voxelmorph, Cyclemorph, and Transmorph showed average DSCs of 0.80±0.08, 0.78±0.11, and 0.78±0.09, respectively. Additionally, the deep learning DIR algorithms demonstrated faster transformation times compared to other models, including commercial and conventional mathematical algorithms (Voxelmorph: 0.36 sec/images, Cyclemorph: 0.3 sec/images, Transmorph: 5.1 sec/images, SyN: 140 sec/images, NiftyReg: 40.2 sec/images). In conclusion, this study highlights the varying clinical applicability of deep learning-based DIR methods in different anatomical regions. While challenges were encountered in head and neck CT registrations, 4D lung CT registrations exhibited favorable results, indicating the potential for clinical implementation. Further research and development in DIR algorithms tailored to specific anatomical regions are warranted to improve the overall clinical utility of these methods.

• Journal of Biomedical Engineering Research
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• v.17 no.4
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• pp.433-442
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• 1996

Since one texture property(i.e coarseness, orientation, regularity, granularity) for ultrasound liver ages was not sufficient enough to classify the characteristics of livers, we used multi texture vectors tracted from ultrasound liver images and a statistical classifier. Multi texture vectors are selected among the feature vectors of the normal liver, fat liver and cirrhosis images which have a good separability in those ultrasound liver images. The statistical classifier uses multi texture vectors as input vectors and classifies ultrasound liver images for each multi texture vector by the Bayes decision rule. Then the decision of the liver disease is made by choosing the maximum value from the averages of a posteriori probability for each multi texture vector In our simulation, we obtained higtler correct ratio than that of other methods using single feature vector, for the test set the correct ratio is 94% in the normal liver, 84% in the fat liver and 86% in the cirrhosis liver.

• Journal of Biomedical Engineering Research
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• v.7 no.1
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• pp.45-52
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• 1986

A digital X-ray system was developed using photo-diode arrays. Images were collected with 1,024x1,024x10 bit resolution and 0.5 sec acquisition time. An4 collected images were processed and restored using computer algorithms. For the normal and the patient, we obtained the digital X-ray images using the developed system.