• Proceedings of the Korea Information Processing Society Conference
• /
• 2007.05a
• /
• pp.326-327
• /
• 2007

We investigated whether the CT images of hepatic lesions could be analyzed by computer-aided diagnosis (CAD) tool. We retrospectively reanalyzed 14 liver CT images (10 hepatocellular cancers and 4 benign liver lesions; patients who presented with hepatic masses). The hepatic lesions on CT were segmented by rectangular ROI technique and the morphologic features were extracted and quantitated using fractal texture analysis. The contrast enhancement of hepatic lesions was also quantified and added to the differential diagnosis. The best discriminating function combining the textural features and the values of contrast enhancement of the lesions was created using linear discriminant analysis. Textural feature analysis showed moderate accuracy in the differential diagnosis of hepatic lesions, but statistically insignificant. Combining textural analysis and contrast enhancement value resulted in improved diagnostic accuracy, but further studies are needed.

• Journal of Korean Neurosurgical Society
• /
• v.53 no.1
• /
• pp.39-42
• /
• 2013

Objective : Obtaining real-time image is essential for neurosurgeons to minimize invasion of normal brain tissue and to prompt diagnosis of intracranial event. The aim of this study was to report our three-year experience with a mobile computed tomography (mCT) for intraoperative and bedside scanning. Methods : A total of 357 mCT (297 patients) scans from January 2009 to December 2011 in single institution were reviewed. After excluding postoperative routine follow-up, 202 mCT were included for analysis. Their medical records such as diagnosis, clinical application, impact on decision making, times, image quality and radiologic findings were assessed. Results : Two-hundred-two mCT scans were performed in the operation room (n=192, 95%) or intensive care unit (ICU) (n=10, 5%). Regarding intraoperative images, extent of resection of tumor (n=55, 27.2%), degree of hematoma removal (n=42, 20.8%), confirmation of catheter placement (n=91, 45.0%) and monitoring unexpected complications (n=4, 2.0%) were evaluated. A total of 14 additional procedures were introduced after confirmation of residual tumor (n=7, 50%), hematoma (n=2, 14.3%), malpositioned catheter (n=3, 21.4%) and newly developed intracranial events (n=2, 14.3%). Every image was obtained within 15 minutes and image quality was sufficient for interpretation. Conclusion : mCT is feasible for prompt intraoperative and ICU monitoring with enhanced diagnostic certainty, safety and efficiency.

• International Journal of Control, Automation, and Systems
• /
• v.2 no.1
• /
• pp.100-106
• /
• 2004

In this paper, we present a novel, rapid approach for the detection of brain tumors and deformity boundaries in medical images using a genetic algorithm with wavelet based preprocessing. The contour detection problem is formulated as an optimization process that seeks the contour of the object in a manner of minimizing an energy function based on an active contour model. The brain tumor segmentation contour, however, cannot be detected in case that a higher gradient intensity exists other than the interested brain tumor and deformities. Our method for discerning brain tumors and deformities from unwanted adjacent tissues is proposed. The proposed method can be used in medical image analysis because the exact contour of the brain tumor and deformities is followed by precise diagnosis of the deformities.

• Proceedings of the KOSOMBE Conference
• /
• v.1996 no.11
• /
• pp.357-362
• /
• 1996

A way of quantitatively describing the tissue architecture we have investigated when developing a computer program for malignancy grading of transitional cell bladder carcinoma. The minimum spanning trees, MST was created by connecting the center points of the nuclei in the tissue section image. These nuclei were found by thresholding the image at an automatically determined threshold followed by a connected component labeling and a watershed algorithm for separation of overlapping nuclei. Clusters were defined in the MST by thresholding the edge lengths. For these clusters geometric and densitometric features were measures. These features were compared by multivariate statistical methods to the subjective grading by the pathologists and the resulting correspondence was 85% on a material of 40 samples.

• 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.

• ETRI Journal
• /
• v.46 no.3
• /
• pp.513-525
• /
• 2024

Deep neural networks trained on labeled medical data face major challenges owing to the economic costs of data acquisition through expensive medical imaging devices, expert labor for data annotation, and large datasets to achieve optimal model performance. The heterogeneity of diseases, such as Alzheimer's disease, further complicates deep learning because the test cases may substantially differ from the training data, possibly increasing the rate of false positives. We propose a reconstruction-based self-supervised anomaly detection model to overcome these challenges. It has a dual-subnetwork encoder that enhances feature encoding augmented by skip connections to the decoder for improving the gradient flow. The novel encoder captures local and global features to improve image reconstruction. In addition, we introduce an entropy-based image conversion method. Extensive evaluations show that the proposed model outperforms benchmark models in anomaly detection and classification using an encoder. The supervised and unsupervised models show improved performances when trained with data preprocessed using the proposed image conversion method.

• 대한생식의학회:학술대회논문집
• /
• 2004.06a
• /
• pp.76-76
• /
• 2004

• Korean Journal of Digital Imaging in Medicine
• /
• v.14 no.2
• /
• pp.1-8
• /
• 2012

Purpose : We aim at presenting the optimum radiologic factor through the evaluation of dose variation and of image quality through the use of a grid in Humerus examination and the change of dose because of the change of radiologic factor. Materials and Methods : We divided it in 3 cases: when using a grid or not and when using IP(Image Plate) in a digital system. Also, as fixing kVp to 70kVp it changed mAs, and fixing mAs to 10 it changed kVp, we put up resolution chart and Burger rose phantom on the acrylic phantom of 7cm (the same level of Humerus) to evaluate the dose and image. We used Image J program to evaluate the quantitative resolution of the obtained image, and made the qualitative evaluation and statistical analysis of the image saved in PACS for 20 radiologic technologist with more than 10 years of experience in order of evaluate its contrast. We used SPSS10(SPSS Inc. Chicago, Illinois) for statistical analysis. Results : We observed the analytic result of resolution by the change of kVp that it was $4.539dGycm^2$ in 60kVp and $757.472dGycm^2$ in 75kVp, which increased about 64.6% of dose, while for the resolution it had the pixel value 30.7% better with 851 in 60kVp than 651 in 75kVp. Also, we analyzed the result of resolution by the change of mAs that it was $3.106dGycm^2$ in 5mAs, and $12.470dGycm^2$ in 20mAs, which increased about 400% of dose, while for the resolution DR had 678 in 5mAs, and 724 in 20mAs that increased about 6.8% of resolution. We made the qualitative evaluation of contrast by the change of kVp that DR showed the higher quality than CR, but the contrast by the change of kVp had no special different at the moment of visual evaluation, nor statistically significant difference(P>0.05). We observed the qualitative evaluation of contraste by the change of mAs that the contrast increased as DR increased mAs, and had statistically significant difference(P<0.05). On the other hand, CR had no significant difference for more than 10mAs nor statistically significant difference(P>0.05). Conclusion : In case of some patients with radiographic exposure by the repeated examination such as emergent patient or Follow up patient, they are considered to try to limit the use of a grid, to set kVp under 65kVp in fixed mode, to select less than 10mAs and to reduce the possibility of patient being bombed.

• Korean Journal of Artificial Intelligence
• /
• v.12 no.1
• /
• pp.1-6
• /
• 2024

Recently, various techniques are being applied through the development of medical AI, and research has been conducted on the application of super-resolution AI models. In this study, evaluate the results of the application of the super-resolution AI model to brain CT as the basic data for future research. Acquiring CT images of the brain, algorithm for brain and bone windowing setting, and the resolution was downscaled to 5 types resolution image based on the original resolution image, and then upscaled to resolution to create an LR image and used for network input with the original imaging. The SRCNN model was applied to each of these images and analyzed using PSNR, SSIM, Loss. As a result of quantitative index analysis, the results were the best at 256×256, the brain and bone window setting PSNR were the same at 33.72, 35.2, and SSIM at 0.98 respectively, and the loss was 0.0004 and 0.0003, respectively, showing relatively excellent performance in the bone window setting CT image. The possibility of future studies aimed image quality and exposure dose is confirmed, and additional studies that need to be verified are also presented, which can be used as basic data for the above studies.

• Korean Journal of Radiology
• /
• v.25 no.6
• /
• pp.518-539
• /
• 2024

Coronary computed tomography angiography (CCTA) has emerged as a pivotal tool for diagnosing and risk-stratifying patients with suspected coronary artery disease (CAD). Recent advancements in image analysis and artificial intelligence (AI) techniques have enabled the comprehensive quantitative analysis of coronary atherosclerosis. Fully quantitative assessments of coronary stenosis and lumen attenuation have improved the accuracy of assessing stenosis severity and predicting hemodynamically significant lesions. In addition to stenosis evaluation, quantitative plaque analysis plays a crucial role in predicting and monitoring CAD progression. Studies have demonstrated that the quantitative assessment of plaque subtypes based on CT attenuation provides a nuanced understanding of plaque characteristics and their association with cardiovascular events. Quantitative analysis of serial CCTA scans offers a unique perspective on the impact of medical therapies on plaque modification. However, challenges such as time-intensive analyses and variability in software platforms still need to be addressed for broader clinical implementation. The paradigm of CCTA has shifted towards comprehensive quantitative plaque analysis facilitated by technological advancements. As these methods continue to evolve, their integration into routine clinical practice has the potential to enhance risk assessment and guide individualized patient management. This article reviews the evolving landscape of quantitative plaque analysis in CCTA and explores its applications and limitations.