• Journal of the Korea Society of Computer and Information
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• v.19 no.12
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• pp.11-19
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• 2014

In this paper, we design an intelligent agent system with the data mining module and information fusion module as the core components of the system and investigate the possibility for the medical expert system. In the data mining module, fuzzy neural network, OFUN-NET analyzes multi-view data and produces fuzzy cluster knowledge base. In the information fusion module and application module, they serve the diagnosis result with possibility degree and useful information for diagnosis, such as uncertainty decision status or detection of asymmetry. We also present the experiment results on the BI-RADS-based feature data set selected form DDSM benchmark database. They show higher classification accuracy than conventional methods and the feasibility of the system as a computer aided diagnosis system.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.11
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• pp.4869-4873
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• 2016

Colonoscopy is currently the best technique available for the detection of colon cancer or colorectal polyps or other precursor lesions. Computer aided detection (CAD) is based on very complex pattern recognition. Local binary patterns (LBPs) are strong illumination invariant texture primitives. Histograms of binary patterns computed across regions are used to describe textures. Every pixel is contrasted relative to gray levels of neighbourhood pixels. In this study, colorectal polyp detection was performed with colonoscopy video frames, with classification via J48 and Fuzzy. Features such as color, discrete cosine transform (DCT) and LBP were used in confirming the superiority of the proposed method in colorectal polyp detection. The performance was better than with other current methods.

• Biomedical Science Letters
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• v.18 no.4
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• pp.399-405
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• 2012

The brain is the body's most organized and controlled organ, and it governs various psychological and mental functions. A brain abnormality could greatly affect one's physical and mental abilities, and consequently one's social life. Brain disorders can be broadly categorized into three main afflictions: stroke, brain tumor, and dementia. Among these, stroke is a common disease that occurs owing to a disorder in blood flow, and it is accompanied by a sudden loss of consciousness and motor paralysis. The main types of strokes are infarction and hemorrhage. The exact diagnosis and early treatment of an infarction are very important for the patient's prognosis and for the determination of the treatment direction. In this study, texture features were analyzed in order to develop a prototype auto-diagnostic system for infarction using computer auto-diagnostic software. The analysis results indicate that of the six parameters measured, the average brightness, average contrast, flatness, and uniformity show a high cognition rate whereas the degree of skewness and entropy show a low cognition rate. On the basis of these results, it was suggested that a digital CT image obtained using the computer auto-diagnostic software can be used to provide valuable information for general CT image auto-detection and diagnosis for pre-reading. This system is highly advantageous because it can achieve early diagnosis of the disease and it can be used as supplementary data in image reading. Further, it is expected to enable accurate medical image detection and reduced diagnostic time in final-reading.

• Journal of Korea Multimedia Society
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• v.16 no.5
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• pp.558-565
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• 2013

Mammography, the process of using X-ray to examine the woman breast, is the one of the effective tools for detecting breast cancer at an early state. In screening mammogram, Computer-Aided Detection(CAD) system helps radiologist to diagnose cases by detecting malignant masses. A mass is an important lesion in the breast that can indicate a cancer. Due to various shapes and unclear boundaries of the masses, detecting breast masses is considered a challenging task. To this end, CAD system detects a lot of regions of interest including normal tissues. Thus it is important to develop the well-organized classifier. In this paper, we propose an enhanced sparse representation (SR) based classifier using Fisher discrimination dictionary learning. Experimental results show that the proposed method outperforms the existing support vector machine (SVM) classifier.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.04a
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• pp.190-195
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• 1997

Abstract: In this paper,an autoematic gear inspection system has been been developed using the computer aided vision system. Image processing and data analysis algorithms for gear inspection have been investigated and shown to perform quickly with high accuracy. As a result,dimensions of a gear can be measured upto few micrometer size in real time. In addition, the system can be applied to a practical manufacturing process even under nosiy conditions.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.11a
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• pp.485-489
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• 1996

In this paper, an automatic gear inspection system has been developed using the computer aided vision system. Image processing and data analysis algorithms for gear inspection have been investigated and were shown to perform quickly with high accuracy. As a result, dimensions of a gear can be measured upto few micrometer size in real time. In addition, the system can be applied to a practical manufacturing process even under noisy conditions.

• Korean Journal of Radiology
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• v.23 no.9
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• pp.847-852
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• 2022

• Phonetics and Speech Sciences
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• v.1 no.2
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• pp.15-22
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• 2009

Prominence of speech, which is often called 'accent,' affects the fluency of speaking American English greatly. In this paper, we present an accurate prominence detection method that can be utilized in computer-aided language learning (CALL) systems. We employed pitch movement, overall syllable energy, 300-2200 Hz band energy, syllable duration, and spectral and temporal correlation as features to model the prominence of speech. After the features for vowel syllables of speech were extracted, prominent syllables were classified by SVM (Support Vector Machine). To further improve accuracy, the differences in characteristics of neighboring syllables were added as additional features. We also applied a speech recognizer to extract more precise syllable boundaries. The performance of our prominence detector was measured based on the Intonational Variation in English (IViE) speech corpus. We obtained 84.9% accuracy which is about 10% higher than previous research.

• Proceedings of the KIEE Conference
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• 2006.07d
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• pp.1771-1772
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• 2006

In this paper, we considered multiple faults detection on a coaxial cable through Time-Frequency Domain Reflectometry (TFDR). It is well known that TFDR has high resolution accuracy for detecting and estimating the fault detection on a coaxial cable. This approach was based on time-frequency signal analysis and utilized a chirp signal multiplied by a Gaussian time envelope. The Gaussian envelope provided time localization, while the chirp allowed one to excite the system interest. We carried out experiments with 10C-FBT coaxial cable having either one or two faults. The result shows TFDR can be extended to detect multiple faults with high accuracy on a coaxial cable.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.12
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• pp.1678-1684
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• 2018

Dental caries is one of the most popular oral disease. The aim of automatic dental cavity detection system is helping dentist to make accurate diagnosis. It is very important to separate cavity from the teeth in the detection system. In this paper, We compared two active contour algorithms, Snake and DRLSE(Distance Regularized Level Set Evolution). To improve performance, image is selected ROI(region of interest), then applied bilateral filter, Canny edge. In order to evaluate the algorithms, we applied to 7 tooth phantoms from incisor to molar. Each teeth contains two cavities of different shape. As a result, Snake is faster than DRLSE, but Snake has limitation to compute topology of objects. DRLSE is slower but those of performance is better.