• Biomolecules & Therapeutics
• /
• v.30 no.2
• /
• pp.179-183
• /
• 2022

Traditionally, pathologists microscopically examine tissue sections to detect pathological lesions; the many slides that must be evaluated impose severe work burdens. Also, diagnostic accuracy varies by pathologist training and experience; better diagnostic tools are required. Given the rapid development of computer vision, automated deep learning is now used to classify microscopic images, including medical images. Here, we used a Inception-v3 deep learning model to detect mouse lung metastatic tumors via whole slide imaging (WSI); we cropped the images to 151 by 151 pixels. The images were divided into training (53.8%) and test (46.2%) sets (21,017 and 18,016 images, respectively). When images from lung tissue containing tumor tissues were evaluated, the model accuracy was 98.76%. When images from normal lung tissue were evaluated, the model accuracy ("no tumor") was 99.87%. Thus, the deep learning model distinguished metastatic lesions from normal lung tissue. Our approach will allow the rapid and accurate analysis of various tissues.

• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.46 no.6
• /
• pp.1-6
• /
• 2009

We propose an intensity-based image registration method for medical images. The proposed registration is performed by the use of a new measure based on the entropy of conditional probabilities. To achieve the registration, we define a modified conditional entropy (MCE) computed from the joint histograms for the area intensities of two given images. And we conduct experiments with our method as well as existing methods based on the sum of squared differences (SSD), normalized correlation coefficient (NCC), normalized mutual information (NMI) criteria. We evaluate the precision of SSD-, NCC-, MI- and MCE-based measurements by comparing the registration obtained from the same modality magnetic resonance (MR) images and the different modality transformed MR/transformed CT images. The experimental results show that the proposed method is faster and more accurate than other optimization methods.

• Journal of radiological science and technology
• /
• v.30 no.1
• /
• pp.47-51
• /
• 2007

Development of hospital information system and Picture Archiving Communication System is not new in the medical field, and the development of internet and information technology are also universal. In the course of such development, however, it is hard to share medical information without a refined standard format. Especially in the department of radiology, the role of PACS has become very important in interchanging information with other disparate hospital information systems. A specific system needs to be developed that radiological reports are archived into a database efficiently. This includes sharing of medical images. A model is suggested in this study in which an internal system is developed where radiologists store necessary images and transmit them in the standard international clinical format, Clinical Document Architecture, and share the information with hospitals. CDA document generator was made to generate a new file format and separate the existing storage system from the new system. This was to ensure the access to required data in XML documents. The model presented in this study added a process where crucial images in reading are inserted in the CDA radiological report generator. Therefore, this study suggests a storage and transmission model for CDA documents, which is different from the existing DICOM SR. Radiological reports could be better shared, when the application function for inserting images and the analysis of standard clinical terms are completed.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.7 no.5
• /
• pp.964-971
• /
• 2003

According to the development of information processing technology and computer hardware, PACS systems have been installed in many hospitals. They can increase the efficiency and the convenience remarkably for handling medical images using digitalized data. After we compare the generation images with other cases, we can read the images correctly and decide how to treat the patients. If the results, included test method and specialist's opinion, are represented dynamically on homepage in hospital. then visitors can get their experience in directly and understand the field of examination and the area of medical treatment. In this thesis, we display the effective images such as MR of the abnormal cases according to parts and diseases, the movie and still images such as Angio image, the other multimedia materials such as the sound and text of doctor's opinions, in SMIL based on XML, concerning the problem of concurrency.

• Progress in Medical Physics
• /
• v.22 no.3
• /
• pp.117-123
• /
• 2011

Synchrotron radiation (SR) imaging enables us to observe internal structures of biologic samples without staining. In this study, we obtained X-ray microscopic images of human breast tissues with 11.1 KeV hard X-ray microscope of the Pohang light source and used zone plates and phase-contrast technique to get high resolution X-ray images. Hard X-ray microscopic images of fibrocystic change and breast cancer tissues with a spatial resolution of 60 nm were obtained and from these images, we could observe the micro-structures of human breast tissue. Also we analyzed and compared these images, which revealed distinct features of each condition. In conclusion, SR imaging with phase-contrast hard X-ray microscope for medical application, especially in breast disease can give some useful information for clinical research.

• Korean Journal of Radiology
• /
• v.23 no.5
• /
• pp.500-504
• /
• 2022

• Korean Journal of Radiology
• /
• v.21 no.8
• /
• pp.987-997
• /
• 2020

Objective: Measurement of the liver and spleen volumes has clinical implications. Although computed tomography (CT) volumetry is considered to be the most reliable noninvasive method for liver and spleen volume measurement, it has limited application in clinical practice due to its time-consuming segmentation process. We aimed to develop and validate a deep learning algorithm (DLA) for fully automated liver and spleen segmentation using portal venous phase CT images in various liver conditions. Materials and Methods: A DLA for liver and spleen segmentation was trained using a development dataset of portal venous CT images from 813 patients. Performance of the DLA was evaluated in two separate test datasets: dataset-1 which included 150 CT examinations in patients with various liver conditions (i.e., healthy liver, fatty liver, chronic liver disease, cirrhosis, and post-hepatectomy) and dataset-2 which included 50 pairs of CT examinations performed at ours and other institutions. The performance of the DLA was evaluated using the dice similarity score (DSS) for segmentation and Bland-Altman 95% limits of agreement (LOA) for measurement of the volumetric indices, which was compared with that of ground truth manual segmentation. Results: In test dataset-1, the DLA achieved a mean DSS of 0.973 and 0.974 for liver and spleen segmentation, respectively, with no significant difference in DSS across different liver conditions (p = 0.60 and 0.26 for the liver and spleen, respectively). For the measurement of volumetric indices, the Bland-Altman 95% LOA was -0.17 ± 3.07% for liver volume and -0.56 ± 3.78% for spleen volume. In test dataset-2, DLA performance using CT images obtained at outside institutions and our institution was comparable for liver (DSS, 0.982 vs. 0.983; p = 0.28) and spleen (DSS, 0.969 vs. 0.968; p = 0.41) segmentation. Conclusion: The DLA enabled highly accurate segmentation and volume measurement of the liver and spleen using portal venous phase CT images of patients with various liver conditions.

• Journal of Biomedical Engineering Research
• /
• v.23 no.2
• /
• pp.81-85
• /
• 2002

In this paper, a new fragi1e watermarking algorithm for medical images is proposed. It makes possible to resolve the security and forgery problem of the medical images. In the proposed algorithm. the singularity which represents the inherent characteristic of the medical image is extracted and used as watermark. To extract the singularity point. we adopted Mallat wavelet transform because it can describe the edge of image exactly. Mallat wavelet transform produces horizontal and vertical subbands of the same resolution with the original image. The magnitude and phase components of the edge are obtained using these subbands. Based on the magnitude and phase components. LMM which will be used as watermark is determined. As LMM is the inherent singularity of image, if any forgery is applied to medical image, LMM of original and forged image are different each other Detecting the changes of LMM for the two images makes it possible whether any image is undergone forgery or not From the experimental results, we conformed that the proposed algorithm detects the forged area of the image very well.

• Journal of the Korea Computer Graphics Society
• /
• v.28 no.2
• /
• pp.11-19
• /
• 2022

Recently, various researches on medical image generation have been suggested, and it becomes crucial to accurately evaluate the quality and diversity of the generated medical images. For this purpose, the expert's visual turing test, feature distribution visualization, and quantitative evaluation through IS and FID are evaluated. However, there are few methods for quantitatively evaluating medical images in terms of fidelity and diversity. In this paper, images are generated by learning a chest CT dataset of non-small cell lung cancer patients through DCGAN and PGGAN generative models, and the performance of the two generative models are evaluated in terms of fidelity and diversity. The performance is quantitatively evaluated through IS and FID, which are one-dimensional score-based evaluation methods, and Precision and Recall, Improved Precision and Recall, which are two-dimensional score-based evaluation methods, and the characteristics and limitations of each evaluation method are also analyzed in medical imaging.

• Journal of Korean Neurosurgical Society
• /
• v.66 no.1
• /
• pp.53-62
• /
• 2023

Objective : Deep learning is a machine learning approach based on artificial neural network training, and object detection algorithm using deep learning is used as the most powerful tool in image analysis. We analyzed and evaluated the diagnostic performance of a deep learning algorithm to identify skull fractures in plain radiographic images and investigated its clinical applicability. Methods : A total of 2026 plain radiographic images of the skull (fracture, 991; normal, 1035) were obtained from 741 patients. The RetinaNet architecture was used as a deep learning model. Precision, recall, and average precision were measured to evaluate the deep learning algorithm's diagnostic performance. Results : In ResNet-152, the average precision for intersection over union (IOU) 0.1, 0.3, and 0.5, were 0.7240, 0.6698, and 0.3687, respectively. When the intersection over union (IOU) and confidence threshold were 0.1, the precision was 0.7292, and the recall was 0.7650. When the IOU threshold was 0.1, and the confidence threshold was 0.6, the true and false rates were 82.9% and 17.1%, respectively. There were significant differences in the true/false and false-positive/false-negative ratios between the anterior-posterior, towne, and both lateral views (p=0.032 and p=0.003). Objects detected in false positives had vascular grooves and suture lines. In false negatives, the detection performance of the diastatic fractures, fractures crossing the suture line, and fractures around the vascular grooves and orbit was poor. Conclusion : The object detection algorithm applied with deep learning is expected to be a valuable tool in diagnosing skull fractures.