• The Transactions of the Korean Institute of Electrical Engineers D
• /
• v.52 no.1
• /
• pp.64-75
• /
• 2003

Medical image segmentation is the process by which an original image is partitioned into some homogeneous regions like bones, soft tissues, etc. This study demonstrates an automatic medical image segmentation technique based on independent component analysis. Independent component analysis is a generalization of principal component analysis which encodes the higher-order dependencies in the input in addition to the correlations. It extracts statistically independent components from input data. Use of automatic medical image segmentation technique using independent component analysis under the assumption that medical image consists of some statistically independent parts leads to a method that allows for more accurate segmentation of bones from CT data. The result of automatic segmentation using independent component analysis with square test data was evaluated using probability of error(PE) and ultimate measurement accuracy(UMA) value. It was also compared to a general segmentation method using threshold based on sensitivity(True Positive Rate), specificity(False Positive Rate) and mislabelling rate. The evaluation result was done statistical Paired-t test. Most of the results show that the automatic segmentation using independent component analysis has better result than general segmentation using threshold.

• International Journal of Computer Science & Network Security
• /
• v.21 no.1
• /
• pp.97-106
• /
• 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 Korea Multimedia Society
• /
• v.13 no.11
• /
• pp.1698-1705
• /
• 2010

In this paper, we propose a fast liver segmentation method from magnetic resonance(MR) images. Our method efficiently divides a MR image into a set of discrete objects, and boundaries based on the normalized gradient magnitude information. Then, the objects belonging to the liver are detected by using 2D seeded region growing with seed points, which are extracted from the segmented liver region of the slice immediately above or below the current slice. Finally, rolling ball algorithm, and connected component analysis minimizes false positive error near the liver boundaries. Our method was validated by twenty data sets and the results were compared with the manually segmented result. The average volumetric overlap error was 5.2%, and average absolute volumetric measurement error was 1.9%. The average processing time for segmenting one data set was about three seconds. Our method could be used for computer-aided liver diagnosis, which requires a fast and accurate segmentation of liver.

• Tuberculosis and Respiratory Diseases
• /
• v.62 no.6
• /
• pp.523-530
• /
• 2007

Background: A ThinPrep$^{(R)}$ Processor was developed to overcome the limitations of conventional cytology and is widely used to diagnose various cancers. This study compared the diagnostic efficacy of conventional cytology for lung cancer with that of the ThinPrep$^{(R)}$ cytology using the bronchial washing fluid. Methods: The bronchial washing fluid of 790 patients from Jan. 2002 to Dec. 2006, who were suspected of gaving a lung malignancy, was evaluated. Both ThinPrep$^{(R)}$ and conventional cytology were performed for all specimens. Result: Four hundred forty-six men and 344 women were enrolled in this study, and 197 of them were diagnosed with cancer from either a bronchoscopic biopsy or a percutaneous needle aspiration biopsy. ThinPrep$^{(R)}$ cytology showed a sensitivity, specificity, positive predictive value, negative predictive value and false negative error rate of 71.1%, 98.0%, 92.1%, 91.1%, 8.9%, respectively. The conventional cytology showed sensitivity, specificity, positive predictive value, nagative predictive value and false negative error rate of 57.9%, 98.0%, 90.5%, 87.5%, 12.5%, respectively. For central lesions, the sensitivity of conventional cytology and ThinPrep$^{(R)}$ were 70.1% and 82.8%, respectively. Conclusion: ThinPrep$^{(R)}$ cytology showed a higher sensitivity and lower false negative error rate than conventional cytology. This result was unaffected by the histological classification of lung cancer. Therefore, ThinPrep$^{(R)}$ cytology appears to be a useful method for increasing the detection rate of lung cancer in bronchial washing cytology test.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.14 no.7
• /
• pp.1551-1556
• /
• 2010

In this paper, we propose a new inspection method that applies fuzzy reasoning method considering the difference of brightness and intensity of illumination by bend together. In the preprocessing phase, we compensate the degree of semiconductor images with bilinear interpolation and moment-rotation. Then we use fuzzy reasoning method with the difference of brightness from error region by pattern matching and the difference of intensity of illumination from bends. Then the result is difuzzified and applied to the final inspection process. In experiment which uses 30 real world semiconductors with strait shots and side shots, the proposed method successfully discard the false positive identified by conventional brightness comparison only method without any loss of misidentification.

• Proceedings of the KOSOMBE Conference
• /
• v.1997 no.05
• /
• pp.358-361
• /
• 1997

This paper describes a multichannel epileptic seizure detection algorithm based on wavelet transform(WT), artificial neural network(ANN) and expert system. First, through the WT, a small number of wavelet coefficients is used to represent the single channel epileptic spike. Next, 3-layer feed-forward network employing the error back propagation algorithm is trained and tested using parameters obtained above. Finally, 16 channel expert system which is based on clinical experience is introduced as a artifact rejection and reliable detection. The suggested algorithm was implemented on personal computer(PC). Two main events i.e., epileptiform and normal activities, were selected from 32 person's EEGs(normal: 20, seizure disorder: 12) in consensus among experts. The result was that WT reduced data input size and ANN detected 97 of the 100 EEGs containing definite spike - sensitivity of 97%. Expert rule system was capable of rejecting a wide variety of artifacts commonly found in EEG recordings. It also reduced false positive detections of ANN.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.17 no.10
• /
• pp.2788-2808
• /
• 2023

Brain tumors are one of the most threatening malignancies for humans. Misdiagnosis of brain tumors can result in false medical intervention, which ultimately reduces a patient's chance of survival. Manual identification and segmentation of brain tumors from Magnetic Resonance Imaging (MRI) scans can be difficult and error-prone because of the great range of tumor tissues that exist in various individuals and the similarity of normal tissues. To overcome this limitation, the Amended Convolutional Neural Network (ACNN) model has been introduced, a unique combination of three techniques that have not been previously explored for brain tumor detection. The three techniques integrated into the ACNN model are image tissue preprocessing using the Kalman Bucy Smoothing Filter to remove noisy pixels from the input, image tissue segmentation using the Isotonic Regressive Image Tissue Segmentation Process, and feature extraction using the Marr Wavelet Transformation. The extracted features are compared with the testing features using a sigmoid activation function in the output layer. The experimental findings show that the suggested model outperforms existing techniques concerning accuracy, precision, sensitivity, dice score, Jaccard index, specificity, Positive Predictive Value, Hausdorff distance, recall, and F1 score. The proposed ACNN model achieved a maximum accuracy of 98.8%, which is higher than other existing models, according to the experimental results.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.18 no.3
• /
• pp.720-737
• /
• 2024

Private set intersection cardinality (PSI-CA) is a typical problem in the field of secure multi-party computation, which enables two parties calculate the cardinality of intersection securely without revealing any information about their sets. And it is suitable for private data protection scenarios where only the cardinality of the set intersection needs to be calculated. However, most of the currently available PSI-CA protocols only meet the security under the semi-honest model and can't resist the malicious behaviors of participants. To solve the problems above, by the application of the variant of Elgamal cryptography and Bloom filter, we propose an efficient PSI-CA protocol with high security. We also present two new operations on Bloom filter called IBF and BIBF, which could further enhance the safety of private data. Using zero-knowledge proof to ensure the safety under malicious adversary model. Moreover, in order to minimize the error in the results caused by the false positive problem, we use Garbled Bloom Filter and key-value pair packing creatively and present an improved PSI-CA protocol. Through experimental comparison with several existing representative protocols, our protocol runs with linear time complexity and more excellent characters, which is more suitable for practical application scenarios.

• Journal of Biomedical Engineering Research
• /
• v.30 no.6
• /
• pp.516-520
• /
• 2009

The purpose of this study is to determine optimal filtering condition and threshold for the detection of gait-cycles for various walkway slopes as well as gait velocities. Ten young healthy subjects with accelerometer system on thigh and ankle walked on a treadmill at 9 conditions (three speeds and three slopes) for 5 minutes. Two direction signals, i.e. anterior-posterior (AP) and superior-inferior (SI) directions, of each sensor (four sensor orientations) were used to detect specific events of gait cycle. Variation of the threshold (from -1G to 1G) and lowpass cutoff frequency (fc) were applied to the event detection and their performance was evaluated according to the error index (EI), which was defined as the combination of the accuracy and false positive rate. Optimal fc and threshold were determined for each slope in terms of the EI. The optimal fc, threshold and their corresponding EI depended much on the walkway slope so that their coefficients of variation (CV) ranged 19~120%. When all data for 3 slopes were used in the identification of optimal conditions for each sensor, the best error indices for all sensor orientations were comparable ranging 1.43~1.76%, but the optimal fc and threshold depended much on the sensor position. The result indicates that the gait-cycle detection robust to walkway slope is possible by threshold method with well-defined filtering condition and threshold.

• Journal of radiological science and technology
• /
• v.39 no.4
• /
• pp.527-534
• /
• 2016

Dual-energy X-ray absorptiometry (DEXA) is the most widely used technical instrument for evaluating bone mineral content (BMC) and density (BMD) in patients of all ages. In 2016, DEXA devices operating is 5617 in Korea. In this study we investigated the quality of management practices survey for DEXA equipment and we analyzed it. We got a survey response rate of 12.6%. Accurate bone densitometry test is used data for estimation a patient's risk of fracture. However, improper bone densitometry will increase the possibility of causing a false positive. Therefore. it is essential to use the proper aids accurate bone densitomenty to be performed, and the quality control of the device to reduce the error factor of the tester through the training to reduce error for the device and the attitude.