• Steel and Composite Structures
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• v.29 no.6
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• pp.703-716
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• 2018

Rapid detection of damages in civil engineering structures, in order to assess their possible disorders and as a result produce competent decision making, are crucial to ensure their health and ultimately enhance the level of public safety. In traditional intelligent health monitoring methods, the features are manually extracted depending on prior knowledge and diagnostic expertise. Inspired by the idea of unsupervised feature learning that uses artificial intelligence techniques to learn features from raw data, a two-stage learning method is proposed here for intelligent health monitoring of civil engineering structures. In the first stage, $Nystr{\ddot{o}}m$ method is used for automatic feature extraction from structural vibration signals. In the second stage, Moving Kernel Principal Component Analysis (MKPCA) is employed to classify the health conditions based on the extracted features. In this paper, KPCA has been implemented in a new form as Moving KPCA for effectively segmenting large data and for determining the changes, as data are continuously collected. Numerical results revealed that the proposed health monitoring system has a satisfactory performance for detecting the damage scenarios of a three-story frame aluminum structure. Furthermore, the enhanced version of KPCA methods exhibited a significant improvement in sensitivity, accuracy, and effectiveness over conventional methods.

• Journal of Biomedical Engineering Research
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• v.42 no.5
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• pp.201-210
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• 2021

In Korean medicine, tongue diagnosis is one of the important diagnostic methods for diagnosing abnormalities in the body. Representative features that are used in the tongue diagnosis include color, shape, texture, cracks, and tooth marks. When diagnosing a patient through these features, the diagnosis criteria may be different for each oriental medical doctor, and even the same person may have different diagnosis results depending on time and work environment. In order to overcome this problem, recent studies to automate and standardize tongue diagnosis using machine learning are continuing and the basic process of such a machine learning-based tongue diagnosis system is tongue segmentation. In this paper, image data is augmented based on the main tongue features, and backbones of various famous deep learning architecture models are used for automatic tongue segmentation. The experimental results show that the proposed augmentation technique improves the accuracy of tongue segmentation, and that automatic tongue segmentation can be performed with a high accuracy of 99.12%.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.3
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• pp.161-169
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• 2023

Degenerative arthritis is a common joint disease that affects many elderly people and is typically diagnosed through radiography. However, the need for remote diagnosis is increasing because knee pain and walking disorders caused by degenerative arthritis make face-to-face treatment difficult. This study collects three-dimensional joint coordinates in real time using Azure Kinect DK and calculates 6 gait features through visualization and one-way ANOVA verification. The random forest classifier, trained with these characteristics, classified degenerative arthritis with an accuracy of 97.52%, and the model's basis for classification was identified through classification algorithm by features. Overall, this study not only compensated for the shortcomings of existing diagnostic methods, but also constructed a high-accuracy prediction model using statistically verified gait features and provided detailed prediction results.

• Journal of the Korean Society of Radiology
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• v.82 no.4
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• pp.838-850
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• 2021

The incidence and prevalence of nontuberculous mycobacterial pulmonary disease (NTM-PD) is increasing worldwide, including in Korea, and the clinical importance of NTM-PD is also rapidly increasing. The diagnosis and management of NTM-PD is difficult. Radiologic evidence is mandatory to diagnose NTM-PD, and the radiologic findings may be the first evidence of the disease in many patients. Traditionally, NTM-PD demonstrates two different radiologic forms: fibrocavitary and nodular bronchiectatic. However, the disease also shows non-specific and a wide spectrum of radiologic features. Radiologists must be aware of the radiologic features of NTM-PD and should include them in the differential diagnosis. This review focuses on the epidemiology in Korea, diagnostic criteria, and radiological features of NTM-PD for radiologists.

• Imaging Science in Dentistry
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• v.54 no.2
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• pp.207-210
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• 2024

Ameloblastic fibrodentinoma (AFD) is a rare benign odontogenic tumor that resembles an ameloblastic fibroma with dysplastic dentin. This report presents a rare case of mandibular AFD with imaging features in a young patient. Panoramic radiography and computed tomography revealed a well-defined lesion with internal septa and calcified foci, causing inferior displacement of the adjacent molars as well as buccolingual cortical thinning and expansion of the posterior mandible. The lesion was surgically removed via mass excision, and the involved tooth was extracted under general anesthesia. During the 5-year follow-up period, no evidence of recurrence was observed. Radiologic features of AFD typically reveal a moderately to well-defined mixed lesion with varying degrees of radiopacity, reflecting the extent of dentin formation. Radiologists should consider AFD in the differential diagnosis when encountering a multilocular lesion with little dense radiopacity, particularly if it is associated with delayed eruption, impaction, or absence of involved teeth, on radiographic images of young patients.

• Korean Journal of Radiology
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• v.21 no.10
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• pp.1115-1125
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• 2020

Combined hepatocellular-cholangiocarcinoma (cHCC-CCA) is a primary liver cancer (PLC) with both hepatocytic and cholangiocytic phenotypes. Recently, the World Health Organization (WHO) updated its histological classification system for cHCC-CCA. Compared to the previous WHO histological classification system, the new version no longer recognizes subtypes of cHCC-CCA with stem cell features. Furthermore, some of these cHCC-CCA subtypes with stem cell features have been recategorized as either hepatocellular carcinomas (HCCs) or intrahepatic cholangiocarcinomas (ICCs). Additionally, distinctive diagnostic terms for intermediate cell carcinomas and cholangiolocarcinomas (previous cholangiolocellular carcinoma subtype) are now recommended. It is important for radiologists to understand these changes because of its potential impact on the imaging-based diagnosis of HCC, particularly because cHCC-CCAs frequently manifest as HCC mimickers, ICC mimickers, or as indeterminate on imaging studies. Therefore, in this review, we introduce the 2019 WHO classification system for cHCC-CCA, illustrate important imaging features characteristic of its subtypes, discuss the impact on imaging-based diagnosis of HCC, and address other important considerations.

• Journal of the Korea Society of Computer and Information
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• v.11 no.1 s.39
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• pp.139-145
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• 2006

Feature extraction technique in digital images has many applications such as robot vision, medical diagnostic system, and motion video transmission, etc. There are several methods for extracting features in digital images for example nonlinear gradient, nonlinear laplacian, and entropy convolutional filter. However, conventional convolutional filters are usually not efficient to extract features in an image because image feature formation in eyes is more sensitive to dark regions than to bright regions. A few nonlinear filters using difference between arithmetic mean and harmonic mean in a window for extracting sketch features are described in this paper They have some advantages, for example simple computation, dependence on local intensities and less sensitive to small intensity changes in very dark regions. Experimental results demonstrate more successful features extraction than other conventional filters over a wide variety of intensity variations.

• Journal of Physiology & Pathology in Korean Medicine
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• v.30 no.1
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• pp.1-6
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• 2016

In recent years, there were frequent exogenous contagious diseases in Eastasia like SARS(severe acute respiratory syndrome), Avian influenza, Swine influenza, MERS etc. But there are various interpretations about their pathological differentiations and lead to controversy to diagnosis and medicinal use. So there needs universal and consistent understanding methods. Several conclusions are obtained from the research on differentiation theories of various epidemic diseases. Essential elements of differential diagnostic system are pathogen, characters and matters of disease and loci, especially three yin and three yang has close affinity with constitutional features or body shape. Binding these 3 categories, an integrated differentiation 3 dimensional coordinates are made. Out of these, each elements of 3 pathogen-axial lines are related with names of exogenous disease, and those of 3 feature-axial lines are related with 8 principal patterns. And those of 3 locus-axial lines implicating therapeutic method are related with steps and location of exterior and interior, 3 yin 3 yang, Defense, Qi, Nutrient and Blood, five viscera and six bowels and tissues. Additionally, 3 lines of each axis consist of factors which have their own affinity each other, so classification of pathogen, feature, locus of disease has layered interconnectedness. This classification system is included in constitutional features of individual patient. Afterwards, these cognitive structure can be used as a general theory guiding method of therapy, prevention and aftercure healthcare.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.7
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• pp.3179-3183
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• 2014

Background: The purpose of study was to evaluate radiologic or clinical features of breast cancer undergoing ultrasound (US)-guided 14G core needle biopsy (CNB) and analyze the differences between underestimated and accurately diagnosed groups. Materials and Methods: Of 1,898 cases of US-guided 14G CNB in our institute, 233 cases were proven to be cancer by surgical pathology. The pathologic results from CNB were invasive ductal carcinoma (IDC) (n=157), ductal carcinoma in situ (DCIS) (n=40), high-risk lesions in 22 cases, and benign in 14 cases. Among high-risk lesions, 7 cases of atypical ductal hyperplasia (ADH) were reported as cancer and 11 cases of DCIS were proven IDC in surgical pathology. Some 29 DCIS cases and 157 cases of IDC were correctly diagnosed with CNB. The clinical and imaging features between underestimated and accurately diagnosed breast cancers were compared. Results: Of 233 cancer cases, underestimation occurred in 18 lesions (7.7%). Among underestimated cancers, CNB proven ADH (n=2) and DCIS (n=11) were diagnosed as IDC and CNB proven ADH (n=5) were diagnosed at DCIS finally. Among the 186 accurately diagnosed group, the CNB results were IDC (n=157) and DCIS (n=29). Comparison of underestimated and accurately diagnosed groups for BI-RADS category, margin of mass on mammography and US and orientation of lesion on US revealed statistically significant differences. Conclusions: Underestimation of US-guided 14G CNB occurred in 7.7% of breast cancers. Between underestimated and correctly diagnosed groups, BI-RADS category, margin of the mass on mammography and margin and orientation of the lesions on US were different.

• Korean Journal of Head & Neck Oncology
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• v.13 no.2
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• pp.180-186
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• 1997

Objectives: Salivary gland tumors pose considerable difficulty in diagnostic and prognostic assessment based on the histopathologic features alone. We studied the expression of type IV collagen and fibronectin in salivary gland tumors with special emphasis on the differential diagnostic significance. Materials and Methods: We did immunohistochemical stain on paraffin embedded tissues of 33 benign and 24 malignant salivary gland tumors using monoclonal antibody for type IV collagen and polyclonal antibody for fibronectin. Results: 1) Well preserved linear basement membrane-like staining of type IV collagen was detected in duct-cell-derived benign salivary gland tumors. But pleomorphic adenoma exhibited a heterogeneous pattern as focal augmentation and interruption. 2) In malignant tumors, type IV collagen was distributed in an irregular, interrupted manner or completely absent. Adenoid cystic carcinomas displayed a marked staining of the basal membrane associated substances in the pseudocysts. 3) The staining pattern of fibronectin was similar to that of type IV collagen execpt more dense in the stroma. 4) Salivary gland tumors which have a prominent myoepithelial cell component revealed a particular deposition of basement membrane materials adjacent to the myoepithelial cells. Conclusion: The study of the basal membrane substances may be helpful for differential diagnosis of benign and malignant salivary gland tumors and identifying special features of salivary gland tumors such as pseudocystic pattern of adenoid cystic carcinoma. Also we think that the myoepithelial cells contribute to the formation of basement membrane materials.