• The Korean Journal of Cytopathology
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• v.4 no.1
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• pp.1-8
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• 1993

This study was performed in order to evaluate the accuracy and the usefulness of the fine needle aspiration cytology (FNAC) on the breast lesions, to compare the FNAC findings between fibroadenoma and fibrocystic disease, and to determine the accuracy of cytologic Black's nuclear grading. The subjects in this study were 110 cases of FNAC, later confirmed by biopsy, between January 1988 and December 1991. The results are as follows ; 1 Comparison between the results of the FNAC and the histologic findings revealed that FNAC had a sensitivity of 96.6%, a specificity of 100%, a false negative rate of 3.4% a false positive rate of 0.0%, and an overall diagnostic accuracy of 98.2%. 2 Semi-quantitative evaluation of epithelial celluarity, stroma, and naked nuclei in the smears of aspirate showed high celluarity in 56.7% of the aspirates from fibroadenoma and in 0% of those from fibrocystic disease. Abundant stroma was found in 46.7% of the fibroadenoma and none of fibrocystic disease. Numerous naked nuclei were found in 60% of the fibroadenoma and 4.5% of the fibrocystic disease. The overall diagnostic accuracy was 98% 3. In order to determine the accuracy of Black's nuclear grading of FNAC on breast carcinoma, we retrospectively studied 38 cases of ductal carcinomas diagnosed by FNAC with subsequent histologic confirmation. The concordance rate with histology was 94.7%. These results suggest that FNAC of breast is a diagnostically accurate method, and provide for the preoperative differential diagnosis between fibroadenoma and fibrocystic disease. Our results also suggest that the evaluation of nuclear grading of FNAC can predict clinical outcome and decide the way of management of breast cancer.

• International Journal of Automotive Technology
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• v.8 no.4
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• pp.493-504
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• 2007

This paper proposes a stereo vision-based forward obstacle detection and distance measurement method. In general, stereo vision-based obstacle detection methods in automotive applications can be classified into two categories: IPM (Inverse Perspective Mapping)-based and disparity histogram-based. The existing disparity histogram-based method was developed for stop-and-go applications. The proposed method extends the scope of the disparity histogram-based method to highway applications by 1) replacing the fixed rectangular ROI (Region Of Interest) with the traveling lane-based ROI, and 2) replacing the peak detection with a constant threshold with peak detection using the threshold-line and peakness evaluation. In order to increase the true positive rate while decreasing the false positive rate, multiple candidate peaks were generated and then verified by the edge feature correlation method. By testing the proposed method with images captured on the highway, it was shown that the proposed method was able to overcome problems in previous implementations while being applied successfully to highway collision warning/avoidance conditions, In addition, comparisons with laser radar showed that vision sensors with a wider FOV (Field Of View) provided faster responses to cutting-in vehicles. Finally, we integrated the proposed method into a longitudinal collision avoidance system. Experimental results showed that activated braking by risk assessment using the state of the ego-vehicle and measuring the distance to upcoming obstacles could successfully prevent collisions.

• Journal of Korea Multimedia Society
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• v.18 no.10
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• pp.1197-1204
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• 2015

Smoke detection plays an important role for the early detection of fire. In this paper, we suggest a newly developed method that generated LBPV(Local Binary Pattern Variance)s as special feature vectors from RGB contrast images can be applied to detect smoke using SVM(Support Vector Machine). The proposed method rearranges mean value of the block from each R, G, B channel and its intensity of the mean value. Additionally, it generates RGB contrast image which indicates each RGB channel’s contrast via smoke’s achromatic color. Uniform LBPV, Rotation-Invariance LBPV, Rotation-Invariance Uniform LBPV are applied to RGB Contrast images so that it could generate feature vector from the form of LBP. It helps to distinguish between smoke and non smoke area through SVM. Experimental results show that true positive detection rate is similar but false positive detection rate has been improved, although the proposed method reduced numbers of feature vector in half comparing with the existing method with LBP and LBPV.

• International Journal of Computer Science & Network Security
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• v.21 no.1
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• pp.97-106
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• 2021

Social networking platforms have become a smart way for people to interact and meet on internet. It provides a way to keep in touch with friends, families, colleagues, business partners, and many more. Among the various social networking sites, Twitter is one of the fastest-growing sites where users can read the news, share ideas, discuss issues etc. Due to its vast popularity, the accounts of legitimate users are vulnerable to the large number of threats. Spam and Malware are some of the most affecting threats found on Twitter. Therefore, in order to enjoy seamless services it is required to secure Twitter against malicious users by fixing them in advance. Various researches have used many Machine Learning (ML) based approaches to detect spammers on Twitter. This research aims to devise a secure system based on Hybrid Similarity Cosine and Soft Cosine measured in combination with Genetic Algorithm (GA) and Artificial Neural Network (ANN) to secure Twitter network against spammers. The similarity among tweets is determined using Cosine with Soft Cosine which has been applied on the Twitter dataset. GA has been utilized to enhance training with minimum training error by selecting the best suitable features according to the designed fitness function. The tweets have been classified as spammer and non-spammer based on ANN structure along with the voting rule. The True Positive Rate (TPR), False Positive Rate (FPR) and Classification Accuracy are considered as the evaluation parameter to evaluate the performance of system designed in this research. The simulation results reveals that our proposed model outperform the existing state-of-arts.

• Journal of information and communication convergence engineering
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• v.21 no.4
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• pp.322-328
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• 2023

The number of senior citizens with large bowel obstruction is steadily growing in Korea. Plain radiography was used to examine the severity and treatment of this phenomenon. To avoid examiner subjectivity in radiography readings, we propose an automatic segmentation method to identify fluid-filled areas indicative of large bowel obstruction. Our proposed method applies the Hough transform to locate suspicious areas successfully and applies the possibilistic fuzzy c-means unsupervised learning algorithm to form the target area in a noisy environment. In an experiment with 104 real-world large-bowel obstruction radiographs, the proposed method successfully identified all suspicious areas in 73 of 104 input images and partially identified the target area in another 21 images. Additionally, the proposed method shows a true-positive rate of over 91% and false-positive rate of less than 3% for pixel-level area formation. These performance evaluation statistics are significantly better than those of the possibilistic c-means and fuzzy c-means-based strategies; thus, this hybrid strategy of automatic segmentation of large bowel suspicious areas is successful and might be feasible for real-world use.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.4
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• pp.1693-1697
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• 2014

Background: The Bahcesehir Breast Cancer Screening Project is the first organized population based breast cancer mammographic screening project in Turkey. The objective of this prospective observational study was to demonstrate the feasibility of a screening program in a developing country and to determine the appropriate age (40 or 50 years old) to start with screening in Turkish women. Materials and Methods: Between January 2009 to December 2010, a total of 3,758 women aged 40-69 years were recruited in this prospective study. Screening was conducted biannually, and five rounds were planned. After clinical breast examination (CBE), two-view mammograms were obtained. True positivity, false positivity, positive predictive values (PPV) according to ACR, cancer detection rate, minimal cancer detection rate, axillary node positivity and recall rate were calculated. Breast ultrasound and biopsy were performed in suspicious cases. Results: Breast biopsy was performed in 55 patients, and 18 cancers were detected in the first round. The overall cancer detection rate was 4.8 per 1,000 women. Most of the screened women (54%) and detected cancers (56%) were in women aged 40-49. Ductal carcinoma in situ (DCIS) and stage I cancer and axillary node positivity rates were 22%, 61%, and 16.6%, respectively. The positive predictivity for biopsy was 32.7%, whereas the overall recall rate was 18.4 %. Conclusions: Preliminary results of the study suggest that population based organized screening are feasible and age of onset of mammographic screening should be 40 years in Turkey.

• Korean Journal of Radiology
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• v.22 no.11
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• pp.1764-1776
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• 2021

Objective: This study aimed to validate a deep learning-based fully automatic calcium scoring (coronary artery calcium [CAC]_auto) system using previously published cardiac computed tomography (CT) cohort data with the manually segmented coronary calcium scoring (CAC_hand) system as the reference standard. Materials and Methods: We developed the CAC_auto system using 100 co-registered, non-enhanced and contrast-enhanced CT scans. For the validation of the CAC_auto system, three previously published CT cohorts (n = 2985) were chosen to represent different clinical scenarios (i.e., 2647 asymptomatic, 220 symptomatic, 118 valve disease) and four CT models. The performance of the CAC_auto system in detecting coronary calcium was determined. The reliability of the system in measuring the Agatston score as compared with CAC_hand was also evaluated per vessel and per patient using intraclass correlation coefficients (ICCs) and Bland-Altman analysis. The agreement between CAC_auto and CAC_hand based on the cardiovascular risk stratification categories (Agatston score: 0, 1-10, 11-100, 101-400, > 400) was evaluated. Results: In 2985 patients, 6218 coronary calcium lesions were identified using CAC_hand. The per-lesion sensitivity and false-positive rate of the CAC_auto system in detecting coronary calcium were 93.3% (5800 of 6218) and 0.11 false-positive lesions per patient, respectively. The CAC_auto system, in measuring the Agatston score, yielded ICCs of 0.99 for all the vessels (left main 0.91, left anterior descending 0.99, left circumflex 0.96, right coronary 0.99). The limits of agreement between CAC_auto and CAC_hand were 1.6 ± 52.2. The linearly weighted kappa value for the Agatston score categorization was 0.94. The main causes of false-positive results were image noise (29.1%, 97/333 lesions), aortic wall calcification (25.5%, 85/333 lesions), and pericardial calcification (24.3%, 81/333 lesions). Conclusion: The atlas-based CAC_auto empowered by deep learning provided accurate calcium score measurement as compared with manual method and risk category classification, which could potentially streamline CAC imaging workflows.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.3
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• pp.75-81
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• 2014

In a distribution of the digital image, there is a serious problem that is distributed an illegal forgery image by pirates. For the problem solution, this paper proposes an image forensic decision algorithm using an edge energy information of forgery image. The algorithm uses SA (Streaking Artifacts) and SPAM (Subtractive Pixel Adjacency Matrix) to extract the edge energy informations of original image according to JPEG compression rate(QF=90, 70, 50 and 30) and the query image. And then it decides the forge whether or not by comparing the edge informations between the original and query image each other. According to each threshold in TCJCR (Threshold by Combination of JPEG Compression Ratios), the matching of the edge informations of original and query image is excused. Through the matching experiments, TP (True Positive) and FN (False Negative) is 87.2% and 13.8% respectively. Thus, the minimum average decision error is 0.1349. Also, it is confirmed that the performed class evaluation of the proposed algorithm is 'Excellent(A)' because of the AUROC (Area Under Receiver Operating Characteristic) curve is 0.9388 by sensitivity and 1-specificity.

• Biomedical Science Letters
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• v.21 no.1
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• pp.9-14
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• 2015

Mouse hepatitis virus (MHV) is a major pathogen in laboratory mice that usually leads to fatal diseases, such as hepatitis, multiple sclerosis, encephalitis, and respiratory disease. MHV has a high infection rate, and it needs to be detected as soon as possible to prevent its spread to other facilities. However, MHV detection by enzyme-linked immunosorbent assay (ELISA) often gives false positives; thus, it is very important that the results are confirmed as true positives in the early infection stage or distinguished as false positives with more accurate, reliable methods. Under microbiological screening, MHV ELISA-positive mice were found in four GFP-tagging transgenic mice. To verify the detection of the MHV antigen directly, reverse transcription polymerase chain reaction (RT-PCR) was performed, and the mice were determined to be MHV negative. Additional serum antibody-based screening was conducted with three different ELISA kits, and multiplexed fluorometric immunoassay (MFIA) was performed to confirm their accuracy/sensitivity. In brief, the ELISA kit for A59 nucleocapsid protein (MHV-A59N) revealed MHV ELISA positivity, while other ELISA kits (MHV-S lysate and MHV-JHM lysate) demonstrated MHV negativity. In MFIA, only the test for the recombinant A59 nucleocapsid antigen was MHV positive, which was consistent with the ELISA results. These results suggest that the ELISA kit with the recombinant A59 nucleocapsid antigen might induce non-specific MHV ELISA positivity and that confirmation is therefore essential.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.24
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• pp.10665-10673
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• 2015

Background: We wished to analyse patterns of use of needle biopsy procedures by BreastScreen Australia (BSA) accredited programs to identify areas for improvement. Design: BSA services provided anonymous data regarding percutaneous needle biopsy of screen detected lesions assessed between 2005-2009. Results: 12 services, from 5 of 7 Australian states and territories provided data for 18212 lesions biopsied. Preoperative diagnosis rates were 96.84% for lesion other than microcalcification (LOTM) and 93.21% for microcalcifications. At surgery 97.9% impalpable lesions were removed at the first procedure. Of 11548 Microcalcification (LOTM) biopsied, 46.9% were malignant. The final diagnosis was reached by conventional core biopsy (CCB) in 72.46%, FNAB in 21.33%, VACB in 1.69% and open biopsy in 4.52% of lesions. FNA is being limited to LOTM with benign imaging After FNAB, core biopsy was required for 38% of LOTM. In LOTM the mean false positive rate (FPR) was 0.36% for FNAB, 0.06% for NCB and 0% for VACB. Diagnostic accuracy was 72.75% for FNAB and 92.1% for core biopsies combined. Of 6441 microcalcifications biopsied 2305 (35.8%) were malignant. Microcalcifications are being assessed primarily by NCB but 6.57% underwent FNAB, 45.6% of which required NCB. False positive diagnoses were rare. FNR was 5% for NCB and 1.53% for VACB. Diagnostic accuracy was 73.52% for FNAB, 86.29% for NCB and 88.63% for VACB. Only 8 of 12 services had access to VACB facilities. Conclusions: BSA services are selecting lesions effectively for biopsy and are achieving high preoperative diagnosis rates. Gaps in the present accreditation standards require further consideration.