• International Journal of Computer Science & Network Security
• /
• v.24 no.1
• /
• pp.9-16
• /
• 2024

AI or Artificial Intelligence has been a significant tool used in the organisational backgrounds for an effective improvement in the management methods. The processing of the information and the analysis of the data for the further achievement of heightened efficiency can be performed by AI through its data analytics measures. In the medical field, AI has been integrated for an improvement within the management of the medical services and to note a rise in the levels of customer satisfaction. With the benefits of reasoning and problem solving, AI has been able to initiate a range of benefits for both the consumers and the medical personnel. The main benefits which have been noted in the integration of AI would be integrated into the study. The issues which are noted with the integrated AI usage for the medical sector would also be identified in the study. Medical Image Processing has been seen to integrate 3D image datasets with the medical industry, in terms of Computed Tomography (CT) or Magnetic Resonance Imaging (MRI). The usage of such medical devices have occurred in the diagnosis of the patients, the development of guidance towards medical intervention and an overall increase in the medical efficiency. The study would focus on such different tools, adhered with AI for increased medical improvement.

• Proceedings of the KSMRM Conference
• /
• 2004.09a
• /
• pp.23-32
• /
• 2004

The chemotherapy sensitive Lewis lung carcinoma (LLC) and chemotherapy resistant Lewis lung carcinoma (CR-LLC) tumors concurrently implanted in mice, and compare these findings with histological macroscopic observations against 3D reconstruction of Fluorescence Molecular Tomography (FMT) preformed in vivo on the same animals. For the 3D image reconstruction we used 32 laser source images, a flat image and 3D surface rendering that confused for 3D Fluorescence Molecular Imaging (FMI). A minimum of ten tissue sections were analyzed per tumor for quantification of the TUNEL-positive cells, cell-associated Cy5.5-Annexin and vessel-associated Alexa Fluor-Lectin. These are useful apoptosis and angiogenesis markers, and they serve as validation experiments to data obtained in vivousing a Cy5.5-Annexin V conjugate injected intravenously in chemotherapy-treated animals carrying the tumors studied histologically. We detected higher levels of apoptosis and corresponding higher levels of Cy5.5 fluorescence in the LLC vs. the CR-LLC tumors according to tissue depth and these findings confirm that in vivo staining with the Cy5.5-Annexing conjugate correlates well with in vitro TUNEL staining and is consistent with the higher apoptotic index expected from the LLC line. There appeared to be 1.38% more apoptosis for LLC than CR-LLC. Consequently there is good correlation between the histology results and in vivo fluorescence-mediated optical imaging. In conclusion the apoptotic images of 3D FMI were validated by microscopic histological image analysis. This is a significant result for the continuous progress of fluorescence 3D imaging research.

• Electronics and Telecommunications Trends
• /
• v.35 no.5
• /
• pp.112-122
• /
• 2020

In 3D computer graphics, a depth map is an image that provides information related to the distance from the viewpoint to the subject's surface. Stereo sensors, depth cameras, and imaging systems using an active illumination system and a time-resolved detector can perform accurate depth measurements with their own light sources. The 3D image information obtained through the depth map is useful in 3D modeling, autonomous vehicle navigation, object recognition and remote gesture detection, resolution-enhanced medical images, aviation and defense technology, and robotics. In addition, the depth map information is important data used for extracting and restoring multi-view images, and extracting phase information required for digital hologram synthesis. This study is oriented toward a recent research trend in deep learning-based 3D data analysis methods and depth map information extraction technology using a convolutional neural network. Further, the study focuses on 3D image processing technology related to digital hologram and multi-view image extraction/reconstruction, which are becoming more popular as the computing power of hardware rapidly increases.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2019.05a
• /
• pp.432-435
• /
• 2019

Convolutional Neural Networks (CNNs) have recently been gaining popularity in the medical image analysis field because of their image segmentation capabilities. In this paper, we present a CNN that performs automated brain tumor segmentations of sparsely annotated 3D Magnetic Resonance Imaging (MRI) scans. Our CNN is based on 3D U-net architecture, and it includes separate Dilated and Depth-wise Convolutions. It is fully-trained on the BraTS 2018 data set, and it produces more accurate results even when compared to the winners of the BraTS 2017 competition despite having a significantly smaller amount of parameters.

• Journal of Internet Computing and Services
• /
• v.24 no.4
• /
• pp.85-92
• /
• 2023

Deep learning applications are increasingly being leveraged for disease detection tasks in medical imaging modalities such as X-ray, Computed Tomography (CT), and Magnetic Resonance Imaging (MRI). Most data-centric deep learning challenges necessitate the use of supervised learning methodologies to attain high accuracy and to facilitate performance evaluation through comparison with the ground truth. Supervised learning mandates a substantial amount of image and label sets, however, procuring an adequate volume of medical imaging data for training is a formidable task. Various data augmentation strategies can mitigate the underfitting issue inherent in supervised learning-based models that are trained on limited medical image and label sets. This research investigates the enhancement of a deep learning-based rib fracture segmentation model and the efficacy of data augmentation techniques such as left-right flipping, rotation, and scaling. Augmented dataset with L/R flipping and rotations(30°, 60°) increased model performance, however, dataset with rotation(90°) and ⨯0.5 rescaling decreased model performance. This indicates the usage of appropriate data augmentation methods depending on datasets and tasks.

• Journal of the Korean Society of Radiology
• /
• v.11 no.5
• /
• pp.371-377
• /
• 2017

General phantom for practical X-ray photography Practical phantom is an indispensable textbook for radiology, but it is difficult for existing commercially available phantom to be equipped with various kinds of phantom because it is an expensive import. Using 3D printing technology, I would like to make the general phantom for practical X-ray photography less expensive and easier. We would like to use a skeleton model that was produced based on CT image data using a 3D printer of FDM (Fused Deposition Modeling) method as a phantom for general X-ray imaging. 3D slicer 4.7.0 program is used to convert CT DICOM image data into STL file, convert it to G-code conversion process, output it to 3D printer, and create skeleton model. The phantom of the completed phantom was photographed by X - ray and CT, and compared with actual medical images and phantoms on the market, there was a detailed difference between actual medical images and bone density, but it could be utilized as a practical phantom. 3D phonemes that can be used for general X-ray practice can be manufactured at low cost by utilizing 3D printers which are low cost and distributed and free 3D slicer program for research. According to the future diversification and research of 3D printing technology, it will be possible to apply to various fields such as health education and medical service.

• The KIPS Transactions:PartB
• /
• v.11B no.4
• /
• pp.403-410
• /
• 2004

Recently, studies of image analysis, as the preprocessing stage for medical image analysis or image retrieval, are actively carried out. This paper intends to propose a way of utilizing color components for image retrieval. For image retrieval, it is based on color components, and for analysis of color, CLCM (Color Level Co-occurrence Matrix) and statistical techniques are used. CLCM proposed in this paper is to project color components on 3D space through geometric rotate transform and then, to interpret distribution that is made from the spatial relationship. CLCM is 2D histogram that is made in color model, which is created through geometric rotate transform of a color model. In order to analyze it, a statistical technique is used. Like CLCM, GLCM (Gray Level Co-occurrence Matrix)[1] and Invariant Moment [2,3] use 2D distribution chart, which use basic statistical techniques in order to interpret 2D data. However, even though GLCM and Invariant Moment are optimized in each domain, it is impossible to perfectly interpret irregular data available on the spatial coordinates. That is, GLCM and Invariant Moment use only the basic statistical techniques so reliability of the extracted features is low. In order to interpret the spatial relationship and weight of data, this study has used Principal Component Analysis [4,5] that is used in multivariate statistics. In order to increase accuracy of data, it has proposed a way to project color components on 3D space, to rotate it and then, to extract features of data from all angles.

• Journal of Biomedical Engineering Research
• /
• v.19 no.2
• /
• pp.199-210
• /
• 1998

In this paper, we present a 3D reconstruction method of color volume data for a computerized human atlas. Binary volume rendering which takes the advantages of object-order ray traversal and run-length encoding visualizes 3D organs at an interactive speed in a general PC without the help of specific hardwares. This rendering method improves the rendering speed by simplifying the determination of the pixel value of an intermediate depth image and applying newly developed normal vector calculation method. Moreover, we describe the 3D boundary encoding that reduces the involved data considerably without the penalty of image quality. The interactive speed of the binary rendering and the storage efficiency of 3D boundary encoding will accelerate the development of the PC-based human atlas.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.8 no.1
• /
• pp.191-197
• /
• 2013

Conventional CT and MRI scans produce cross-section slices of body that are viewed sequentially by radiologists who must imagine or extrapolate from these views what the 3 dimensional anatomy should be. By using sophisticated algorithm and high performance computing, these cross-sections may be rendered as direct 3D representations of human anatomy. The 2D medical image analysis forced to use time-consuming, subjective, error-prone manual techniques, such as slice tracing and region painting, for extracting regions of interest. To overcome the drawbacks of 2D medical image analysis, combining with medical image processing, 3D visualization is essential for extracting anatomical structures and making measurements. We used the gray-level thresholding, region growing, contour following, deformable model to segment human organ and used the feature vectors from texture analysis to detect harmful cancer. We used the perspective projection and marching cube algorithm to render the surface from volumetric MR and CT image data. The 3D visualization of human anatomy and segmented human organ provides valuable benefits for radiation treatment planning, surgical planning, surgery simulation, image guided surgery and interventional imaging applications.

• Korean Journal of Head & Neck Oncology
• /
• v.26 no.2
• /
• pp.198-203
• /
• 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.