• 한국멀티미디어학회논문지
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• 제17권1호
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• pp.1-7
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• 2014

Hippocampus is an important part of brain which is related with early memory storage and spatial navigation. By observing the anatomy of hippocampus, some brain diseases effecting human memory (e.g. Alzheimer, schizophrenia, etc.) can be diagnosed and predicted earlier. The diagnosis process is highly related with hippocampus segmentation. In this paper, hippocampus segmentation using Active Shape Model, which not only works based on image intensity, but also by using prior knowledge of hippocampus shape and intensity from the training images, is proposed. The results show that ASM is applicable in segmenting hippocampus from whole brain MR image. It also shows that adding more images in the training set results in better accuracy of hippocampus segmentation.

• 한국멀티미디어학회논문지
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• 제19권8호
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• pp.1236-1247
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• 2016

Recently, the level set has become a popular method in many research fields. The main reason is that it can be modified into many variants. One such case is our proposed method. We describe a contrast-enhancement method to segment the hippocampal region from the background. However, the hippocampus region has quite similar intensities to the neighboring pixel intensities. In addition, to handle the inhomogeneous intensities of the hippocampus, we used a bias correction before hippocampal segmentation. Thus, we developed a contrast-enhanced bias-corrected distance-regularized level set (CBDLS) to segment the hippocampus in magnetic resonance imaging (MRI). It shows better performance than the distance-regularized level set evolution (DLS) and bias-corrected distance-regularized level set (BDLS) methods in 33 MRI images of one normal patient. Segmentation after contrast enhancement and bias correction can be done more accurately than segmentation while not using a bias-correction method and without contrast enhancement.

• 한국멀티미디어학회논문지
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• 제15권9호
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• pp.1075-1085
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• 2012

영상분할은 의료 임상연구에서 가장 중요한 과정 중의 하나이다. 특히 뇌 MRI영상에서 해마의 위축은 알츠하이머병 진행과정의 초기 특정 표지자로서 해마의 볼륨은 초기 알츠하이머병의 임상적 진단에 도움이 된다. 정확한 볼륨 측정에 있어서 해마 영역의 분할은 중요한 역할을 한다. 하지만 MRI 영상에서 해마영역은 낮은 대조도, 낮은 신호 대 잡음 비율, 불연속성 경계의 특징을 보이며, 이러한 특징들은 MRI 영상에서 해마의 정확한 분할을 어렵게 만든다. 이 문제를 해결하기 위해 전처리 과정으로 실험영상에서 관심영역을 선택한 후 반전영상과 원본영상과의 차영상 대조도를 향상시킨 후 비등방성 확산(Anisotropic diffusion) 필터링, 가우시안(Gaussian) 필터링을 수행하였다. 마지막으로 두 개의 레벨 셋(Level Set)기반의 동적 윤곽선(Active Contour) 모델을 결합하여 해마를 분할하는 방법을 제안하였다. 제안된 해마분할방법의 유효성을 다양한 방법으로 평가한 결과 제안된 해마분할방법은 분할 속도와 정확도 면에서 뚜렷하게 개선이 되었음을 확인하였다. 결론적으로 제안된 방법이 해마와 같은 특징을 가진 영역을 분할하는데 적합하다고 할 수 있다. 향후 다른 연구 기법들과 결합할 경우 더욱 잠재성이 증대될 수 있을 것이다.

• 한국멀티미디어학회논문지
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• 제15권12호
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• pp.1449-1455
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• 2012

This paper has developed a system for early diagnosis of senile dementia and mild cognitive impairment (MCI) by developing software to measure the volume of hippocampus. This software consists of two parts; segmentation and analysis. The segmentation part uses ROI and region growing to segment hippocampus region. On the other hand, the analysis part creates a volume rendering of hippocampus. This software is expected contribute in these research fields for dementia diagnosis and its medication planning.

• 한국멀티미디어학회논문지
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• 제17권5호
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• pp.566-572
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• 2014

The hippocampus has been known as one of the most important structure related to many neurological disorders, such as Alzheimer's disease. This paper presents the snake model to segment hippocampus from brain MRI. The snake model or active contour model is widely used in medical image processing fields, especially image segmentation they look onto nearby edge, localizing them accurately. We applied a snake model on brain MRI. Then we compared our results with an active shape approach. The results show that hippocampus was successfully segmented by the snake model.

• 한국멀티미디어학회논문지
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• 제18권7호
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• pp.827-833
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• 2015

In this paper, we have compared three level set-based active contour (LSAC) methods on inhomogeneous MR image segmentation which is known as an important role of brain diseases to diagnosis and treatment in early. MR image is often occurred a problem with similar intensities and weak boundaries which have been causing many segmentation methods. However, LSAC method could be able to segment the targets such as the level set based on the local image fitting energy, the local binary fitting energy, and local Gaussian distribution fitting energy. Our implemented and tested the subcortical image segmentations were the corpus callosum and hippocampus and finally demonstrated their effectiveness. Consequently, the level set based on local Gaussian distribution fitting energy has obtained the best model to accurate and robust for the subcortical image segmentation.

• 대한의용생체공학회:학술대회논문집
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• 대한의용생체공학회 1997년도 춘계학술대회
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• pp.116-118
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• 1997

In hippocampal morphology Abnormalities, including unilateral or bilateral volume loss, are known to occur in epilepsy, Alzheimer's disease, and in certain amnestic syndromes. To detect such abnormalities in hippocampal morphology, we present a method that combines region growing and dynamic contour model to detect hippocampus from MRI brain data. The segmentation process is performed two steps. First region growing with a seed point is performed in the region of hippocampus and the initial contour of dynamic contour model is obtained. Second, the initial contour is modified on the basis of criteria that integrate energy with contour smoothness and the image gradient along the contour. As a result, this method improves fairly sensitivity to the choice of the initial seed point, which is often seen by conventional contour model. The power and practicality of this method have been tested on two brain datasets. Thus, we have developed an effective algorithm to extract hippocampus from MRI brain data.

• 한국멀티미디어학회논문지
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• 제18권2호
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• pp.120-127
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• 2015

Mild Cognitive Impairment(MCI) is a prior step to Alzheimer's Disease(AD). It is different from AD which is seriously affecting daily life. Particularly, the hippocampus could be charged a crucial function for forming memory. MCI has a high risk about progress to AD. Our investigated research for a relationship between hippocampus and AD has been studied. The measurement of hippocampus volumetric is one of the most commonly used method. The three dimensional reconstructed medical images could be passible to interpret and its examination in various aspects but the cost of brain research with the medical equipment is very high. In this study, 3D visualization was performed from a series of brain Magnetic Resonance Images(MRI) and we have designed and implemented a competitive software tool based on the open libraries of Visualization ToolKit(VTK). Consequently, our visualization software tool could be useful to various medical fields and specially prognosis and diagnosis for MCI patients.

• 한국멀티미디어학회:학술대회논문집
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• 한국멀티미디어학회 2012년도 춘계학술발표대회논문집
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• pp.268-268
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• 2012

• Investigative Magnetic Resonance Imaging
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• 제25권3호
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• pp.164-171
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• 2021

Purpose: Mild cognitive impairment (MCI) is a prodromal stage of Alzheimer's disease (AD). Brain atrophy in this disease spectrum begins in the medial temporal lobe structure, which can be recognized by magnetic resonance imaging. To overcome the unsatisfactory inter-observer reliability of visual evaluation, quantitative brain volumetry has been developed and widely investigated for the diagnosis of MCI and AD. The aim of this study was to assess the prediction accuracy of quantitative brain volumetry using a fully automated segmentation software package, NeuroQuant^®, for the diagnosis of MCI. Materials and Methods: A total of 418 subjects from the Korean Brain Aging Study for Early Diagnosis and Prediction of Alzheimer's Disease cohort were included in our study. Each participant was allocated to either a cognitively normal old group (n = 285) or an MCI group (n = 133). Brain volumetric data were obtained from T1-weighted images using the NeuroQuant software package. Logistic regression and receiver operating characteristic (ROC) curve analyses were performed to investigate relevant brain regions and their prediction accuracies. Results: Multivariate logistic regression analysis revealed that normative percentiles of the hippocampus (P < 0.001), amygdala (P = 0.003), frontal lobe (P = 0.049), medial parietal lobe (P = 0.023), and third ventricle (P = 0.012) were independent predictive factors for MCI. In ROC analysis, normative percentiles of the hippocampus and amygdala showed fair accuracies in the diagnosis of MCI (area under the curve: 0.739 and 0.727, respectively). Conclusion: Normative percentiles of the hippocampus and amygdala provided by the fully automated segmentation software could be used for screening MCI with a reasonable post-processing time. This information might help us interpret structural MRI in patients with cognitive impairment.