• Proceedings of the KOSOMBE Conference
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• v.1995 no.05
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• pp.227-229
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• 1995

This paper attempts to examine an optimal method for the chromosome specific vector extraction. Usually, represented method are used with a line segmentation on a chromosome Image. It is not Inaccurate but also needs a long time for the analysis. This paper purpose to aquire specific vector in the image with a using optimal ellipse estimation method. Normally, shapes of chromosomes are curved and too difficult to analyze automatically. A chromosome has a lot of band which looks like an ellipse. If we can estimate their bands with an ellipse, we can reconstruct the sample Which Is straight and can be analyzed easily. We have rearranged a chromosome Image with above proposed. Result shows a reconstructed sample which Is simple for chromosome analysis.

• Journal of Biomedical Engineering Research
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• v.24 no.4
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• pp.259-266
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• 2003

Non-rigid registration between different modality images with shape deformation can be used to diagnosis and study for inter-patient image registration, longitudinal intra-patient registration, and registration between a patient image and an atlas image. This paper proposes a hierarchical registration method using bodily tissue based segmentation for registration between color images and CT images of the Visible Human leg areas. The cross-sectional color images and the axial CT images are segmented into three distinctive bodily tissue regions, respectively: fat, muscle, and bone. Each region is separately registered hierarchically. Bounding boxes containing bodily tissue regions in different modalities are initially registered. Then, boundaries of the regions are globally registered within range of searching space. Local boundary segments of the regions are further registered for non-rigid registration of the sampled boundary points. Non-rigid registration parameters for the un-sampled points are interpolated linearly. Such hierarchical approach enables the method to register images efficiently. Moreover, registration of visibly distinct bodily tissue regions provides accurate and robust result in region boundaries and inside the regions.

• Journal of the Korean Society of Radiology
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• v.83 no.6
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• pp.1229-1239
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• 2022

Recently, artificial intelligence (AI) technology has shown potential clinical utility in a wide range of MRI fields. In particular, AI models for improving the efficiency of the image acquisition process and the quality of reconstructed images are being actively developed by the MR research community. AI is expected to further reduce acquisition times in various MRI protocols used in clinical practice when compared to current parallel imaging techniques. Additionally, AI can help with tasks such as planning, parameter optimization, artifact reduction, and quality assessment. Furthermore, AI is being actively applied to automate MR image analysis such as image registration, segmentation, and object detection. For this reason, it is important to consider the effects of protocols or devices in MR image analysis. In this review article, we briefly introduced issues related to AI application of MR image acquisition and reconstruction.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.6
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• pp.2240-2245
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• 2010

Myocardial imaging in SPECT (Single Photon Emission Computed tomography) scan of the gamma-ray emitting radiopharmaceuticals to patients after intravenous radiopharmaceuticals evenly spread in the heart region of interest by recording changes in the disease caused by a computer using the PSA test is to diagnose. Containing information on the functional myocardial perfusion imaging is a useful way to examine non-invasive heart disease, but the argument by noise and low resolution of the physical landscape that is difficult to give. For this paper, the level of myocardial imaging by using the three algorithms to split the video into 3-D implementation of the partitioned area to help you read the proposed plan. To solve the difficulty of reading level, interest in using the sheet set, partitioned area of the left ventricle was ranked the partitioned area was modeled as a 3-D images.

• Korean Journal of Radiology
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• v.22 no.3
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• pp.435-441
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• 2021

Objective: To evaluate the usefulness of the ventricular volume percentage quantified using three-dimensional (3D) brain computed tomography (CT) data for interpreting serial changes in hydrocephalus. Materials and Methods: Intracranial and ventricular volumes were quantified using the semiautomatic 3D threshold-based segmentation approach for 113 brain CT examinations (age at brain CT examination ≤ 18 years) in 38 patients with hydrocephalus. Changes in ventricular volume percentage were calculated using 75 serial brain CT pairs (time interval 173.6 ± 234.9 days) and compared with the conventional assessment of changes in hydrocephalus (increased, unchanged, or decreased). A cut-off value for the diagnosis of no change in hydrocephalus was calculated using receiver operating characteristic curve analysis. The reproducibility of the volumetric measurements was assessed using the intraclass correlation coefficient on a subset of 20 brain CT examinations. Results: Mean intracranial volume, ventricular volume, and ventricular volume percentage were 1284.6 ± 297.1 cm³, 249.0 ± 150.8 cm³, and 19.9 ± 12.8%, respectively. The volumetric measurements were highly reproducible (intraclass correlation coefficient = 1.0). Serial changes (0.8 ± 0.6%) in ventricular volume percentage in the unchanged group (n = 28) were significantly smaller than those in the increased and decreased groups (6.8 ± 4.3% and 5.6 ± 4.2%, respectively; p = 0.001 and p < 0.001, respectively; n = 11 and n = 36, respectively). The ventricular volume percentage was an excellent parameter for evaluating the degree of hydrocephalus (area under the receiver operating characteristic curve = 0.975; 95% confidence interval, 0.948-1.000; p < 0.001). With a cut-off value of 2.4%, the diagnosis of unchanged hydrocephalus could be made with 83.0% sensitivity and 100.0% specificity. Conclusion: The ventricular volume percentage quantified using 3D brain CT data is useful for interpreting serial changes in hydrocephalus.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.9 no.1
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• pp.61-66
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• 2015

Chest X-ray images is the most common and widely used in clinical findings for a wide range of anatomical information about the prognosis of the disease in patients with cardiopulmonary rehabilitation. Many analysis algorithm was developed by a number of studies regarding the region segmentation and image analysis, there are specific differences due to the complexity and diversity of the image. In this paper, a diagnosis support system of the chest X-ray image based on image processing and analysis methods to detect the cardiopulmonary disease. The threshold value and morphological method was applied to segment the pulmonary region in a chest X-ray image. Anatomical measurements and texture analysis was performed on the segmented regions. The effectiveness of the proposed method is shown through experiments and comparison with diagnosis results by clinical experts to show that the proposed method can be used for decision support system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.3
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• pp.958-979
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• 2023

Due to laws, regulations, privacy, etc., between 70-90 percent of providers do not share medical data, forming a "data island". It is essential to collaborate across multiple institutions without sharing patient data. Most existing methods adopt distributed learning and centralized federal architecture to solve this problem, but there are problems of resource heterogeneity and data heterogeneity in the practical application process. This paper proposes a collaborative deep learning modelling method based on the blockchain network. The training process uses encryption parameters to replace the original remote source data transmission to protect privacy. Hyperledger Fabric blockchain is adopted to realize that the parties are not restricted by the third-party authoritative verification end. To a certain extent, the distrust and single point of failure caused by the centralized system are avoided. The aggregation algorithm uses the FedProx algorithm to solve the problem of device heterogeneity and data heterogeneity. The experiments show that the maximum improvement of segmentation accuracy in the collaborative training mode proposed in this paper is 11.179% compared to local training. In the sequential training mode, the average accuracy improvement is greater than 7%. In the parallel training mode, the average accuracy improvement is greater than 8%. The experimental results show that the model proposed in this paper can solve the current problem of centralized modelling of multicenter data. In particular, it provides ideas to solve privacy protection and break "data silos", and protects all data.

• Journal of Biomedical Engineering Research
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• v.45 no.2
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• pp.90-94
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• 2024

Diagnosing wounds presents a significant challenge in clinical settings due to its complexity and the subjective assessments by clinicians. Wound deep learning algorithms quantitatively assess wounds, overcoming these challenges. However, a limitation in existing research is reliance on specific datasets. To address this limitation, we created a comprehensive dataset by combining open dataset with self-produced dataset to enhance clinical applicability. In the annotation process, machine learning based on Gradient Vector Flow (GVF) was utilized to improve objectivity and efficiency over time. Furthermore, the deep learning model was equipped U-net with residual blocks. Significant improvements were observed using the input dataset with images cropped to contain only the wound region of interest (ROI), as opposed to original sized dataset. As a result, the Dice score remarkably increased from 0.80 using the original dataset to 0.89 using the wound ROI crop dataset. This study highlights the need for diverse research using comprehensive datasets. In future study, we aim to further enhance and diversify our dataset to encompass different environments and ethnicities.

• Journal of International Society for Simulation Surgery
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• v.2 no.2
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• pp.90-93
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• 2015

The fibula free flap has now become the most reliable and frequently used option for mandible reconstruction. Recently, three dimensional images and printing technologies are applied to mandibular reconstruction. We introduce our recent experience of mandibular reconstruction using three dimensionally planned fibula free flap in a patient with gunshot injury. The defect was virtually reconstructed with three-dimensional image. Because bone fragments are dislocated from original position, relocation was necessary. Fragments are virtually relocated to original position using mirror image of unaffected right side of the mandible. A medical rapid prototyping (MRP) model and cutting guide was made with 3D printer. Titanium reconstruction plate was adapted to the MRP model manually. 7 cm-sized fibula bone flap was designed on left lower leg. After dissection, proximal and distal margin of fibula flap was osteotomized by using three dimensional cutting guide. Segmentation was also done as planned. The fibula bone flap was attached to the inner side of the prebent reconstruction plate and fixed with screws. Postoperative evaluation was done by comparison between preoperative planning and surgical outcome. Although dislocated condyle is still not in ideal position, we can see that reconstruction was done as planned.

• Journal of Biomedical Engineering Research
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• v.41 no.1
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• pp.48-54
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• 2020

In this paper, we deal with a liver fibrosis classification problem using ultrasound B-mode images. Commonly representative methods for classifying the stages of liver fibrosis include liver biopsy and diagnosis based on ultrasound images. The overall liver shape and the smoothness and roughness of speckle pattern represented in ultrasound images are used for determining the fibrosis stages. Although the ultrasound image based classification is used frequently as an alternative or complementary method of the invasive biopsy, it also has the limitations that liver fibrosis stage decision depends on the image quality and the doctor's experience. With the rapid development of deep learning algorithms, several studies using deep learning methods have been carried out for automated liver fibrosis classification and showed superior performance of high accuracy. The performance of those deep learning methods depends closely on the amount of datasets. We propose an enhanced U-net architecture to maximize the classification accuracy with limited small amount of image datasets. U-net is well known as a neural network for fast and precise segmentation of medical images. We design it newly for the purpose of classifying liver fibrosis stages. In order to assess the performance of the proposed architecture, numerical experiments are conducted on a total of 118 ultrasound B-mode images acquired from 78 patients with liver fibrosis symptoms of F0~F4 stages. The experimental results support that the performance of the proposed architecture is much better compared to the transfer learning using the pre-trained model of VGGNet.