• Journal of the Korea Society of Computer and Information
• /
• v.21 no.12
• /
• pp.11-18
• /
• 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.

• Korean Journal of Radiology
• /
• v.23 no.7
• /
• pp.720-731
• /
• 2022

Objective: We aimed to develop and test a deep learning algorithm (DLA) for fully automated measurement of the volume and signal intensity (SI) of the liver and spleen using gadoxetic acid-enhanced hepatobiliary phase (HBP)-magnetic resonance imaging (MRI) and to evaluate the clinical utility of DLA-assisted assessment of functional liver capacity. Materials and Methods: The DLA was developed using HBP-MRI data from 1014 patients. Using an independent test dataset (110 internal and 90 external MRI data), the segmentation performance of the DLA was measured using the Dice similarity score (DSS), and the agreement between the DLA and the ground truth for the volume and SI measurements was assessed with a Bland-Altman 95% limit of agreement (LOA). In 276 separate patients (male:female, 191:85; mean age ± standard deviation, 40 ± 15 years) who underwent hepatic resection, we evaluated the correlations between various DLA-based MRI indices, including liver volume normalized by body surface area (LV_BSA), liver-to-spleen SI ratio (LSSR), MRI parameter-adjusted LSSR (aLSSR), LSSR × LV_BSA, and aLSSR × LV_BSA, and the indocyanine green retention rate at 15 minutes (ICG-R15), and determined the diagnostic performance of the DLA-based MRI indices to detect ICG-R15 ≥ 20%. Results: In the test dataset, the mean DSS was 0.977 for liver segmentation and 0.946 for spleen segmentation. The Bland-Altman 95% LOAs were 0.08% ± 3.70% for the liver volume, 0.20% ± 7.89% for the spleen volume, -0.02% ± 1.28% for the liver SI, and -0.01% ± 1.70% for the spleen SI. Among DLA-based MRI indices, aLSSR × LV_BSA showed the strongest correlation with ICG-R15 (r = -0.54, p < 0.001), with area under receiver operating characteristic curve of 0.932 (95% confidence interval, 0.895-0.959) to diagnose ICG-R15 ≥ 20%. Conclusion: Our DLA can accurately measure the volume and SI of the liver and spleen and may be useful for assessing functional liver capacity using gadoxetic acid-enhanced HBP-MRI.

• The Korean Journal of Nuclear Medicine Technology
• /
• v.17 no.2
• /
• pp.10-14
• /
• 2013

Purpose: Due to developed simultaneous PET/MRI, it has become possible to obtain more anatomical image information better than conventional PET/CT. By the way, in the PET/CT, the linear absorption coefficient is measured by X-ray directly. However in case of PET/MRI, the value is not measured from MRI images directly, but is calculated by dividing as 4 segmentation ${\mu}-map$. Therefore, in this paper, we will evaluate the SUV's difference of attenuation correction PET images from PET/MRI and PET/CT. Materials and Methods: Biograph mCT40 (Siemens, Germany), Biograph mMR were used as a PET/CT, PET/MRI scanner. For a phantom study, we used a solid type $^{68}Ge$ source, and a liquid type $^{18}F$ uniformity phantom. By using VIBE-DIXON sequence of PET/MRI, human anatomical structure was divided into air-lung-fat-soft tissue for attenuation correction coefficient. In case of PET/CT, the hounsfield unit of CT was used. By setting the ROI at five places of each PET phantom images that is corrected attenuation, the maximum SUV was measured, evaluated %diff about PET/CT vs. PET/MRI. In clinical study, the 18 patients who underwent simultaneous PET/CT and PET/MRI was selected and set the ROI at background, lung, liver, brain, muscle, fat, bone from the each attenuation correction PET images, and then evaluated, compared by measuring the maximum SUV. Results: For solid $^{68}Ge$ source, SUV from PET/MRI is measured lower 88.55% compared to PET/CT. In case of liquid $^{18}F$ uniform phantom, SUV of PET/MRI as compared to PET/CT is measured low 70.17%. If the clinical study, the background SUV of PET/MRI is same with PET/CT's and the one of lung was higher 2.51%. However, it is measured lower about 32.50, 40.35, 23.92, 13.92, 5.00% at liver, brain, muscle, fat, femoral head. Conclusion: In the case of a CT image, because there is a linear relationship between 511 keV ${\gamma}-ray$ and linear absorption coefficient of X-ray, it is possible to correct directly the attenuation of 511 keV ${\gamma}-ray$ by creating a ${\mu}$map from the CT image. However, in the case of the MRI, because the MRI signal has no relationship at all with linear absorption coefficient of ${\gamma}-ray$, the anatomical structure of the human body is divided into four segmentations to correct the attenuation of ${\gamma}-rays$. Even a number of protons in a bone is too low to make MRI signal and to localize segmentation of ${\mu}-map$. Therefore, to develope a proper sequence for measuring more accurate attenuation coefficient is indeed necessary in the future PET/MRI.

• Journal of IKEEE
• /
• v.15 no.3
• /
• pp.191-197
• /
• 2011

An image segmentation model using fuzzy c-means with intuition (FCM-I) model is proposed for the segmentation of magnetic resonance image in this paper. In FCM-I, a measurement called intuition level is adopted so that the intuition level helps to alleviate the effect of noises. A practical magnetic resonance image data set is used for image segmentation experiment and the performance is compared with those of some conventional algorithms. Results show that the segmentation method based on FCM-I compares favorably to several conventional clustering algorithms. Since FCM-I produces cluster prototypes less sensitive to noises and to the selection of involved parameters than the other algorithms, FCM-I is a good candidate for image segmentation problems.

• Journal of KIISE:Software and Applications
• /
• v.27 no.8
• /
• pp.815-826
• /
• 2000

This study is for the segmentation and volume calculation of the white matter and gray matter from brain MRI. In general, the volume of white and gray matter is reduced by contraction of each components in the case of mental retardation which are Alzheimer's disease and Down's syndrome. As results, it is useful for diagnostic and early detection for various mental retardation through the tracing of variation for its volume from the brain MRI. But, until now, it was very difficult to calculate the partial volume of each components existing in some thickness, because MR image was represented by single gray value after scanning by MR scanner. Accordingly, new segmentation algorithm proposed in this paper is to calculate the partial volume of the white and gray matter existing in some thickness through the analysis of the blurred gray value, and is to determine the threshold for segmentation of white and gray matter, and is to calculate the volume of each segmented component. And finally, proposed algorithm was applied the models which was created manually, and then acquired results was compared with that of original model.

• Journal of Korea Multimedia Society
• /
• v.24 no.8
• /
• pp.979-987
• /
• 2021

Multiple Sclerosis (MS) can be early diagnosed by detecting lesions in brain magnetic resonance images (MRI). Unsupervised anomaly detection methods based on autoencoder have been recently proposed for automated detection of MS lesions. However, these autoencoder-based methods were developed only for 2D images (e.g. 2D cross-sectional slices) of MRI, so do not utilize the full 3D information of MRI. In this paper, therefore, we propose a novel 3D autoencoder-based framework for detection of the lesion volume of MS in MRI. We first define a 3D convolutional neural network (CNN) for full MRI volumes, and build each encoder and decoder layer of the 3D autoencoder based on 3D CNN. We also add a skip connection between the encoder and decoder layer for effective data reconstruction. In the experimental results, we compare the 3D autoencoder-based method with the 2D autoencoder models using the training datasets of 80 healthy subjects from the Human Connectome Project (HCP) and the testing datasets of 25 MS patients from the Longitudinal multiple sclerosis lesion segmentation challenge, and show that the proposed method achieves superior performance in prediction of MS lesion by up to 15%.

• Journal of Korea Multimedia Society
• /
• v.17 no.1
• /
• pp.1-7
• /
• 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.

• Proceedings of the IEEK Conference
• /
• 1999.11a
• /
• pp.525-528
• /
• 1999

Abstract - In this paper, a segmentation method for brain Magnetic Resonance(MR) image using region clustering technique with statistical distribution of gradient image and fuzzy rules is described. The brain MRI consists of gray matter and white matter, cerebrospinal fluid. But due to noise, overlap, vagueness, and various parameters, segmentation of MR image is a very difficult task. We use gradient information rather than intensity directly from the MR images and find appropriate thresholds for region classification using gradient approximation, rayleigh distribution function, region clustering, and merging techniques. And then, we propose the adaptive fuzzy rules in order to extract anatomical structures and diseases from brain MR image data. The experimental results shows that the proposed segmentation algorithm given better performance than traditional segmentation techniques.

• Journal of Korea Multimedia Society
• /
• v.17 no.12
• /
• pp.1412-1417
• /
• 2014

The corpus callosum is the largest part of the brain, which is related to many neurological diseases. Snake model or active contour model is widely used in medical image processing field, especially image segmentation they look into the nearby edge, localizing them accurately. In this paper, corpus callosum segmentation using the snake model, is proposed. We tested a snake model on brain MRI. Then we compared the result with an active shape approach and found that snake model had better segmentation accuracy also faster than active shape approach.

• Korean Journal of Computational Design and Engineering
• /
• v.17 no.6
• /
• pp.436-442
• /
• 2012

Traumatic loading during car accidents or sports activities can lead to cervical spinal cord injury. Experiments in spinal cord injury research are mainly carried out on rabbit or rat. Finite element models that include the rat cervical spinal cord and adjacent soft tissues should be developed for efficient studies of mechanisms of spinal cord injury. Images of a rat were obtained from high resolution MRI scanner. Polygonal surfaces were extracted structure by structure from the MRI data using the ITK-SNAP volume segmentation software. These surfaces were converted to Non-uniform Rational B-spline surfaces by the INUS Rapidform rapid prototyping software. Rapidform was also used to generate a thin shell surface model for the dura mater which sheathes the spinal cord. Altair's Hypermesh pre-processor was used to generate finite element meshes for each structure. These processes in this study can be utilized in modeling of other biomedical tissues and can be one of examples for reverse engineering on biomechanics.