• 대한의용생체공학회:의공학회지
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• 제25권5호
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• pp.323-328
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• 2004

대뇌조직 구분을 위한 실험적인 정보를 제공하기 위한 뇌조직 확률 지도를 개발하는 경우 개인마다 구조적으로 다양한 형태를 가진 뇌의 특성과 특히 인종간의 두드러진 차이론 반드시 고려해야 한다 본 연구에서는 특정 그룹에 대한 뇌조직 확률 지도를 제작하는데 필요한 절차를 알아보고 나이에 따른 그룹간의 뇌조직 확률 지도의 구조적인 차이를 살펴보고자 한다 피험자 그룹은 100명의 건강한 한국인이며 나이에 따라 두 그룹으로 분류하였다. 뇌 확률 지도의 기준 좌표계를 설정하기 위해 전체 그룹 내의 모든 피험자의 뇌 영상에 대한 평균 영상을 구하고, 각 뇌 영상을 기준 좌표계로 정규화 시킨다. 정규화 과정에서 얻어진 변환 매개 변수를 미리 각 뇌조직(회질, 백질, 뇌척수액)으로 분할된 피험자의 영상에 적용하고 각 그룹 내에서 변환된 뇌 조직 영상을 평균함으로써 뇌 조직 확률 지도를 완성하였다. 나이에 따른 구조적인 차이를 살펴보기 위해 그룹간 확률 값의 차이 영상을 구하였다. 이전 연구결과에서와 마찬가지로 나이가 증가함에 따라 뇌실이 확대되고 회질의 위축이 전체적인 뇌 영역에서 일어났다. 그러므로 우리는 대뇌 조직 분할을 위해 설험적인 정보들을 사용하고자 할 때는 특정 그룹에 대한 뇌 확률 지도를 사용할 것을 제안한다.

• 한국컴퓨터정보학회논문지
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• 제21권12호
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• pp.11-18
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• 2016

Magnetic resonance image (MRI) is widely used in brain research field and medical image. Especially, non-invasive brain activation acquired image technique, which is functional magnetic resonance image (fMRI) is used in brain study. In this study, we investigate brain activation occurred by LED light stimulation. For investigate of brain activation in experimental small animal, we used high magnetic field 9.4T MRI. Experimental small animal is Balb/c mouse, method of fMRI is using echo planar image (EPI). EPI method spend more less time than any other MRI method. For this reason, however, EPI data has low contrast. Due to the low contrast, image pre-processing is very hard and inaccuracy. In this study, we planned the study protocol, which is called block design in fMRI research field. The block designed has 8 LED light stimulation session and 8 rest session. All block is consist of 6 EPI images and acquired 1 slice of EPI image is 16 second. During the light session, we occurred LED light stimulation for 1 minutes 36 seconds. During the rest session, we do not occurred light stimulation and remain the light off state for 1 minutes 36 seconds. This session repeat the all over the EPI scan time, so the total spend time of EPI scan has almost 26 minutes. After acquired EPI data, we performed the analysis of this image data. In this study, we analysis of EPI data using statistical parametric map (SPM) software and performed image pre-processing such as realignment, co-registration, normalization, smoothing of EPI data. The pre-processing of fMRI data have to segmented using this software. However this method has 3 different method which is Gaussian nonparametric, warped modulate, and tissue probability map. In this study we performed the this 3 different method and compared how they can change the result of fMRI analysis results. The result of this study show that LED light stimulation was activate superior colliculus region in mouse brain. And the most higher activated value of segmentation method was using tissue probability map. this study may help to improve brain activation study using EPI and SPM analysis.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2007년도 Proceedings of ISRS 2007
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• pp.438-441
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• 2007

Recently, due to molecular biology and engineering technology, DNA microarray makes people watch thousands of genes and the state of variation from the tissue samples of living body. With DNA Microarray, it is possible to construct a genetic group that has similar expression patterns and grasp the progress and variation of gene. This paper practices Cluster Analysis which purposes the discovery of biological subgroup or class by using gene expression information. Hence, the purpose of this paper is to predict a new class which is unknown, open leukaemia data are used for the experiment, and MCL (Markov CLustering) algorithm is applied as an analysis method. The MCL algorithm is based on probability and graph flow theory. MCL simulates random walks on a graph using Markov matrices to determine the transition probabilities among nodes of the graph. If you look at closely to the method, first, MCL algorithm should be applied after getting the distance by using Euclidean distance, then inflation and diagonal factors which are tuning modulus should be tuned, and finally the threshold using the average of each column should be gotten to distinguish one class from another class. Our method has improved the accuracy through using the threshold, namely the average of each column. Our experimental result shows about 70% of accuracy in average compared to the class that is known before. Also, for the comparison evaluation to other algorithm, the proposed method compared to and analyzed SOM (Self-Organizing Map) clustering algorithm which is divided into neural network and hierarchical clustering. The method shows the better result when compared to hierarchical clustering. In further study, it should be studied whether there will be a similar result when the parameter of inflation gotten from our experiment is applied to other gene expression data. We are also trying to make a systematic method to improve the accuracy by regulating the factors mentioned above.