• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2010년도 춘계학술발표대회
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• pp.455-458
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• 2010

Extracting the brain from magnetic resonance imaging head scans is an essential preprocessing step of which the accuracy greatly affects subsequent image analysis. The currently popular Brain Extraction Tool produces a brain mask which may be too smooth for practical use to reduce the accuracy. This paper presents a novel and indirect brain extraction method based on non-brain tissue segmentation. Based on ITK, the proposed method allows a non-brain contour by using region growing to match with the original image naturally and extract the brain tissue. Experiments on two set of MRI data and 2D brain image in horizontal plane and 3D brain model indicate successful extraction of brain tissue from a head.

• 대한의용생체공학회:의공학회지
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• 제26권3호
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• pp.129-132
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• 2005

It is one of the most important issues to determine a target brain image that gives a common coordinate system for a constructing population-based brain atlas. The purpose of this study is to provide a simple and reliable procedure that determines the target brain image among the group based on the inherent structural information of three-dimensional magnetic resonance (MR) images. It uses only 11 lines defined automatically as a feature vector representing structural variations based on the Talairach coordinate system. Average characteristic vector of the group and the difference vectors of each one from the average vector were obtained. Finally, the individual data that had the minimum difference vector was determined as the target. We determined the target brain image by both our algorithm and conventional visual inspection for 20 healthy young volunteers. Eighteen fiducial points were marked independently for each data to evaluate the similarity. Target brain image obtained by our algorithm showed the best result, and the visual inspection determined the second one. We concluded that our method could be used to determine an appropriate target brain image in constructing brain atlases such as disease-specific ones.

• 대한의용생체공학회:학술대회논문집
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• 대한의용생체공학회 1997년도 춘계학술대회
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• pp.72-74
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• 1997

We have developed a 3-D image processing and display technique that include image resampling, modification of MIP, and fusion of MIP image and volumetric rendered image. This technique facilitates the visualization of the three-dimensional spatial relationship between vasculature and surrounding organs by overlapping the MIP image on the volumetric rendered image of the organ. We applied this technique to a MR brain image data to produce an MRI angiogram that is overlapped with 3-D volume rendered image of brain. MIP technique was used to visualize the vasculature of brain, and volume rendering was used to visualize the other structures of brain. The two images are fused after adjustment of contrast and brightness levels of each image in such a way that both the vasculature and brain structure are well visualized either by selecting the maximum value of each image or by assigning different color table to each image. The resultant image with this technique visualizes both the brain structure and vasculature simultaneously, allowing the physicians to inspect their relationship more easily. The presented technique will be useful for surgical planning for neurosurgery.

• 핵의학기술
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• 제15권2호
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• pp.60-64
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• 2011

Brain의 질병을 평가하는 유용한 검사방법 중의 하나인 brain perfusion SPECT는 환자의 움직임으로 인한 검사의 실패확률이 높아 one day method를 사용하지 못하고 two days method를 사용해야 하는 경우가 많다. 본 연구에서는 image registration을 사용하여 검사의 실패확률을 줄이고 one day method로 검사를 시행할 수 있는지 image registration을 적용할 경우 검사의 신뢰성을 알아보고자 하였다. Jaszczak phantom에 준비된 방사성동위원소 $^{99m}Tc$을 insert에 111 MBq/mL가 되도록 분배하여 넣고 나머지 background에 3,145 MBq/mL가 되도록 넣어 1:8의 비율로 phantom을 제작하고 Hoffman 2-D brain phantom과 cylindrical uniform phantom에는 111 MBq/mL가 되도록 만든다. 완성된 phantom은 기본 위치에서 frame 당 5 sec씩 총 120 frame을 획득하여 영상을 얻었다. 또 Phantom과 환자의 데이터를 가지고 original 영상과 registration 영상, registration 시행한 후에 original 영상을 subtraction한 영상과 registration하지 않은 영상에서 subtraction한 영상 간의 임의의 같은 위치에 ROI를 설정하고 영상에서 counts 차이를 알아보았다. 실험 결과 약간의 counts 차이를 보였으나 이것은 실험시간이 경과함에 따른 RI의 decay와 phantom의 구조물이 없는 cylindlical phantom에서 조차 약간의 counts의 차이를 보이는 바로 미루어 봤을 때 실험 결과 나온 counts의 차이는 적다고 할 수 있을 것이다. 따라서 registration을 활용하여 brain perfusion SPECT의 단점들을 개선하고 정확한 진단에 도움을 줄 수 있을 것으로 사료된다.

• 신경치료
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• 제22권3호
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• pp.7-10
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• 2018

Purpose The purpose of this study was to investigate the difference of brain activity during action observation training and image training throughout EEG. Methods This study was participated 1 healthy college student without mental illness or cognitive impairment. The subject was randomly selected from university students and was interested in participating in the experiment. The purpose of this study was to investigate the visual and auditory stimuli (action observation) and brain image training. Results The results of our study, EEG value measured o.1 during resting. But brain activity changed to 0.3 during action observation. Finally, it changed to .05 after brain image training. Conclusion EEG measurement results were showed that after watching the Ball squat video, Brain activity increased.

• 대한전자공학회논문지SP
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• 제49권1호
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• pp.81-87
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• 2012

현재 사용되고 있는 두뇌영상의 제거 방법은 비록 환자의 개인 정보를 보호하고 있으나, 과도한 제거로 정확한 두뇌영상의 무결성을 손실할 수 있다. 원래 두뇌의 영상과 동일한 두뇌 조직을 나타내면서 환자의 신원을 감출 수 있는 새로운 익명화 얼굴모델을 생성시키는 방법을 연구하였다. 제안방법은 두 단계로 구성되었다: 10명의 두뇌영상을 정규화시켜서 모조 두뇌 표본 영상을 생성하는 단계와 실험영상 두뇌의 외곽부를 모조 두뇌의 안면부로 대체시키는 단계이다. 전체 두뇌영상에서 두피와 두개골 영역을 분할하기 위하여 레벨셋 알고리즘을 적용하였다. 영역화된 모조 두뇌를 대상 두뇌영상에 동일하게 배치하고 정규화를 시켜서 익명화된 얼굴 모델을 생성하였다. 원래 영상과 변형된 영상의 두뇌 조직부의 밝기 변화를 비교하여 제안 알고리즘의 타당성을 실험하였다. 실험 결과 두 두뇌영상은 두뇌 조직에서 완전히 동일하면서 신원을 파악할 수 없는 것을 검증하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권3호
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• pp.1167-1187
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• 2020

With the development of medical imaging technology, image registration has been widely used in the field of disease diagnosis. The registration between different modal images of brain magnetic resonance (MR) is particularly important for the diagnosis of brain diseases. However, previous registration methods don't take advantage of the prior knowledge of bilateral brain symmetry. Moreover, the difference in gray scale information of different modal images increases the difficulty of registration. In this paper, a multimodal medical image registration method based on image segmentation and symmetric self-similarity is proposed. This method uses modal independent self-similar information and modal consistency information to register images. More particularly, we propose two novel symmetric self-similarity constraint operators to constrain the segmented medical images and convert each modal medical image into a unified modal for multimodal image registration. The experimental results show that the proposed method can effectively reduce the error rate of brain MR multimodal medical image registration with rotation and translation transformations (average 0.43mm and 0.60mm) respectively, whose accuracy is better compared to state-of-the-art image registration methods.

• 한국멀티미디어학회논문지
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• 제25권8호
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• pp.1233-1241
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• 2022

Tumors of the brain are the deadliest, with a life expectancy of only a few years for those with the most advanced forms. Diagnosing a brain tumor is critical to developing a treatment plan to help patients with the disease live longer. A misdiagnosis of brain tumors will lead to incorrect medical treatment, decreasing a patient's chance of survival. Radiologists classify brain tumors via biopsy, which takes a long time. As a result, the doctor will need an automatic classification system to identify brain tumors. Image classification is one application of the deep learning method in computer vision. One of the deep learning's most powerful algorithms is the convolutional neural network (CNN). This paper will introduce a novel deep learning structure and image gradient to classify brain tumors. Meningioma, glioma, and pituitary tumors are the three most popular forms of brain cancer represented in the Figshare dataset, which contains 3,064 T1-weighted brain images from 233 patients. According to the numerical results, our method is more accurate than other approaches.

• 정보처리학회논문지B
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• 제18B권6호
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• pp.385-388
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• 2011

디지털 형태로 저장된 의료정보가 네트워크를 통하여 제약 없이 전송될 수 있게 되면서, 환자의 개인정보 관리는 의료 업계에서 중요한 주제로 부각되었다. 현재 두뇌 영상의 의료정보를 보호하는 방법은 환자의 신원을 은닉시키기 위하여 얼굴을 절삭하는 것이다. 그러나 절삭 과정에서 간혹 중요한 두뇌 조직부가 함께 절단되어 탈면 두뇌 영상은 의료 용도로 활용될 수 없게 손상을 입게 된다. 실린더 모양의 마스크를 덧붙임으로써 두뇌 영상의 중요한 모든 정보를 유지하면서 환자의 신원 정보를 은닉시키는 직접적인 방법을 제안하였다. 제안하는 두뇌 영상의 무손실 변경 방법은 중요한 영상정보가 손상되지 않음을 확인하였다. 또한 마스크로 입혀진 두뇌영상의 신원을 확인할 수 없는 사실도 증명되었다.

• 대한의용생체공학회:학술대회논문집
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• 대한의용생체공학회 1998년도 추계학술대회
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• pp.267-268
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• 1998

In this paper, we analysis 3 types of magnetic resonance image for determining whether brain infarction period is hyperacute or not. If its peirod is hyperacute, we can predict brain infarction transition direction. We use EPI(Echo Planar Image) for prediction of brain infarction transition direction. EPI is a good image for detecting brain infarction because EPI can detect the moving of water in brain which play an important role in deciding method of medical treatment. We utilize characteristics of 3 type of MRI and their relation in brain infarction patient for determining brain infarction period. By this method, we obtain each period characteristics and predict brain infarction transition direction more accurately comparing past method.