• 한국생물정보학회:학술대회논문집
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• 한국생물정보시스템생물학회 2003년도 제2차 연례학술대회 발표논문집
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• pp.170-177
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• 2003

Gene expression data are the quantitative measurements of expression levels and ratios of numberous genes in different situations based on microarray image analysis results. The process to draw meaningful information related to genomic diseases and various biological activities from gene expression data is known as gene expression data analysis. In this paper, we present a hierarchical clustering method of gene expression data based on self organizing map which can analyze the clustering result of gene expression data more efficiently. Using our proposed method, we could eliminate the uncertainty of cluster boundary which is the inherited disadvantage of self organizing map and use the visualization function of hierarchical clustering. And, we could process massive data using fast processing speed of self organizing map and interpret the clustering result of self organizing map more efficiently and user-friendly. To verify the efficiency of our proposed algorithm, we performed tests with following 3 data sets, animal feature data set, yeast gene expression data and leukemia gene expression data set. The result demonstrated the feasibility and utility of the proposed clustering algorithm.

• 대한의용생체공학회:의공학회지
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• 제35권1호
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• pp.1-7
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• 2014

Among semi-quantitative or fully quantitative lateral flow assay readers, an image sensor-based instrument has been widely used because of its simple setup, cheap sensor price, and compact equipment size. For all previous approaches, monochrome CCD or CMOS cameras were used for lateral flow assay imaging in which the overall intensities of all colors were taken into consideration to estimate the analyte content, although the analyte related color information is only limited to a narrow wavelength range. In the present work, we introduced a color CCD camera as a sensor and a color decomposition method to improve the sensitivity of the quantitative biosensor system which utilizes the lateral flow assay successfully. The proposed setup and image processing method were applied to achieve the quantification of imitatively dispensed particles on the surface of a porous membrane first, and the measurement result was then compared with that using a monochrome CCD. The compensation method was proposed in different illumination conditions. Eventually, the color decomposition method was introduced to the commercially available lateral flow immunochromatographic assay for the diagnosis of myocardial infarction. The measurement sensitivity utilizing the color image sensor is significantly improved since the slopes of the linear curve fit are enhanced from 0.0026 to 0.0040 and from 0.0802 to 0.1141 for myoglobin and creatine kinase (CK)-MB detection, respectively.

• 대한의용생체공학회:의공학회지
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• 제7권2호
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• pp.139-146
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• 1986

본 논문은 tms 32010이라는 디지탈 신호 처리용소자를 사용하여 초음하 펄스 도플러 장치를 구현한 것이다. 도플러 장피란 초음파 신호의 송수신 과정에서 발생하는 도플러 효과를 이용하여 혈류의 속도를 측정하는 장치이다. 한 점에서의 속도를 측정하는 단일채널 도플러 장치에서는 실시간 고속 푸리에 변환기를 구현하여 도플러 주하수의 스펙트럼을 측정함으로서 혈류속도를 측정하며 초음파 빔의 일직선상에서의 여러점을 동시에 측정하는 다중채널 도플러 장치에서는 영점교차검출기를 구현하여 평균주파수를 측정하였다. 자중채널 장치는 직렬처리법을 사용하여 하드웨어를 간단히 하였으며 8점에서의 속도를 측정할 수 있도록 하였다.

• 대한의용생체공학회:학술대회논문집
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• 대한의용생체공학회 1994년도 추계학술대회
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• pp.31-38
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• 1994

This paper proposed signal decomposition and multiresolution representation through wavelet transform using wavelet orthonormal basis. And it suggested most appropriate filter for scaling function in multiresoltion representation and compared two compression method, arithmetic coding and Huffman coding. Results are as follows 1. Daub18 coefficient is most appropriate in computing time, energy compaction, image quality. 2. In case of image browsing that should be small in size and good for recognition, it is reasonable to decompose to 3 scale using pyramidal algorithm. 3. For the case of progressive transmittion where requires most grateful image reconstruction from least number of sampls or reconstruction at any target rate, I embedded the data in order of significance after scaling to 5 step. 4. Medical images such as information loss is fatal have to be compressed by lossless method. As a result from compressing 5 scaled data through arithmetic coding and Huffman coding, I obtained that arithmetic coding is better than huffman coding in processing time and compression ratio. And in case of arithmetic coding I could compress to 38% to original image data.

• 대한의용생체공학회:의공학회지
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• 제28권4호
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• pp.477-483
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• 2007

We have measured EOG and fMRI simultaneously to investigate whether eye movement (blinking mechanism) might influence functional magnetic resonance imaging (fMRI) signal response in the primary visual cortex. $T2^*-weighted$ Echo-Planar Imaging (EPI) with concurrent electrooculogram (EOG) was acquired in four subjects while they viewed a fixation point and a checkerboard with a flickering rate of 8Hz. With the help of EOG information we divided the experimental blocks into two different conditions: fixation and moving eye. We have compared the fMRI data of these two conditions. Our results have shown that there is no difference between these two conditions. This might suggest that eye blinking does not affect BOLD signal changes in the primary visual cortex. This means further that eye blinking can be ignored in data processing.

• 대한의용생체공학회:의공학회지
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• 제30권4호
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• pp.306-310
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• 2009

The purpose of this study is to observe the change in gamma-band cortical activities (GBAs) due to task difficulty. Event-related potential and gamma-band activity were investigated using electroencephalograms recorded during auditory oddball tasks with two difficulty levels. For more difficult task, the amplitude of P300 decreased and the peak latency of P300 was delayed significantly compared to easier task. The induced GBA decreased considerably during the P300 latency period and the peak latency of the induced GBA was delayed for more difficult task. The results imply that the difficulty-related change in cortical information processing may be implemented as a change in the strength of local neuronal association.

• 정보처리학회논문지B
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• 제10B권7호
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• pp.769-776
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• 2003

MCP(Maximal Clique Problem)를 해결하기 위해 DNA 컴퓨팅이 사용되고 있다. 그러나 현재의 DNA 컴퓨팅을 MCP에 적용하였을 때, 정점과 간선을 효율적으로 표현할 수 없으며 제한 효소의 잘못된 사용으로 인하여 해를 찾을 수 없는 문제점을 가지고 있다. 본 논문에서는 MCP의 문제점을 해결하기 위해 DNA 컴퓨팅 기법에 DNA 코딩 방법을 적용한 ACO(Algorithm for Code Optimization)를 제안한다. 우리는 ACO를 MCP에 적용하였고, 그 결과 ACO는 Adleman의 DNA 컴퓨팅 알고리즘 보다 가변길이의 DNA 코드를 표현할 수 있으며, 불필요한 정점을 제거한 코드를 생성할 수 있었다. 또한 ACO는 Adleman의 DNA 컴퓨팅 알고리즘 보다 탐색 시간과 생물학적 오류율을 15% 정도 줄임으로써 4배 정도 많은 최종해를 얻을 수 있었다.

• 대한의용생체공학회:학술대회논문집
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• 대한의용생체공학회 1997년도 추계학술대회
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• pp.133-136
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• 1997

The human chromosome analysis is widely used to diagnose genetic disease and various congenital anomalies. Many researches on automated chromosome karyotype analysis has been carried out, some of which produced commercial systems. However, there still remains much room or improving the accuracy of chromosome classification. In this paper, We propose an optimal pattern classifier by neural network to improve the accuracy of chromosome classification. The proposed pattern classifier was built up of multi-step multi-layer neural network(MMANN). We reconstructed chromosome image to improve the chromosome classification accuracy and extracted three morphological features parameters such as centromeric index(C.I.), relative length ratio(R.L.), and relative area ratio(R.A.). This Parameters employed as input in neural network by preprocessing twenty human chromosome images. The experiment results show that the chromosome classification error is reduced much more than that of the other classification methods.

• 대한의용생체공학회:의공학회지
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• 제20권2호
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• pp.149-154
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• 1999

자기공명영상은 다른 의료영상에 비해서 보다 정확한 해부학적인 진단 정보를 제공해 주므로 널리 이용되고 있다. 본 논문에서는 이차원 축단면 뇌 자기총명영상을 분할하는 자동화 알고리즘과 병별에 의해서 손상된 슬라이스를 검출하는 알고리즘을 제안하였다. 영상분활 과정은 두단계로 구성되어 있는데, 첫 단계에서는 이진화와 형태학적 연산을 이용하여 대뇌영역을 추출하고, 둘째 단계에서는 FCM(Fuzzy C-means)알고리즘을 이용하여 추출된 대뇌 내부의 각 조직을 분할하였다. FCM알고리즘은 분할하는 조직의 수가 증가할수록 급격하게 많은 실행시간을 요구하므로 제안하는 두단계 영상분할 과정을 통하여 실행시간을 향상시켰다. 병변 인식은 해부학적지식과 패턴매칭을 이용하였다.

• 대한의용생체공학회:의공학회지
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• 제17권3호
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• pp.365-372
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• 1996

The researches to acquire diagnostic parameters from ultrasonic images are advanced with the progress of the digital image processing technique. Especially, the detection of endocardial boundary is very important in ultrasonic images, because endocardial boundary is used as a clinical parameter to estimate both the cardiac area and the variation of cardiac volume. Various methods to detect cardiac boundary are proposed, but these are insufficient to detect boundary. In this paper, an algorithm that detects the endocardial boundary, expanding the cavity region from the center using statistical information, is proposed The value of mean and sty:nd, wd deviation in cavity region is lower than those in muscle re- gion. Therefore, if we define the multiplication of mean and standard deviation as homogeneous coefficient, it can lead to conclusion that the pixels with small variation of these coefficleno are cavity region, and extraction of endocardial boundary from cavity region is possible. The proposed method detected endocardial boundary more effectively than edge based or threshold based method and is robuster to noise than radial searching method that has high dependency for center position.