• Proceedings of the Korean Society of Medical Physics Conference
• /
• 2002.09a
• /
• pp.341-344
• /
• 2002

Neurodegenerative disorders, like Alzheimer's disease, are often accompanied by reduced brain perfusion (cerebral blood flow). Using the intrinsic magnetic properties of water, arterial spin labeling magnetic resonance imaging (ASLMRI) can map brain perfusion without injection of radioactive tracers or contrast agents. However, accuracy in measuring perfusion with ASL-MRI can be limited because of contributions to the signal from stationary spins and because of signal modulations due to transient magnetic field effects. The goal was to optimize ASL-MRI for perfusion measurements in the aging human brain, including brains with Alzheimer's disease. A new ASL-MRI sequence was designed and evaluated on phantom and humans. Image texture analysis was performed to test quantitatively improvements. Compared to other ASL-MRI methods, the newly designed sequence provided improved signal to noise ratio improved signal uniformity across slices, and thus, increased measurement reliability. This new ASL-MRI sequence should therefore provide improved measurements of regional changes of brain perfusion in normal aging and neurodegenerative disorders.

• 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.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2009.11a
• /
• pp.343-344
• /
• 2009

The aim in this paper is to show how to extract scalp of a series of brain MR images by using region growing segmentation algorithm. Most researches are all forces on the segmentation of skull, gray matter, white matter and CSF. Prior to the segmentation of these inner objects in brain, we segmented the scalp and the brain from the MR images. The scalp mask makes us to quickly exclude background pixels with intensities similar those of the skull, while the brain mask obtained from our brain surface. We make use of connected threshold method (CTM) and confidence connected method (CCM). Both of them are two implementations of region growing in Insight Toolkit (ITK). By using these two methods, the results are displayed contrast in the form of 2D and 3D scalp images.

• Korean Journal of Radiology
• /
• v.2 no.4
• /
• pp.239-242
• /
• 2001

Nonketotic hyperglycinemia (NKH) is a rare metabolic brain disease caused by deficient activity of the glycine cleveage system. Localized proton MR spectroscopy (echo-time 166 msec), performed in an infant with the typical clinical and biochemical features of neonatal NKH, showed a markedly increased peak intensity at 3.55 ppm, which was assigned to glycine. Serial proton MR spectrosocpic studies indicated that glycine/choline and glycine/total creatine ratios correlated closely with the patient's clinical course. Proton MR spectroscopy was useful for the non-invasive detection and monitoring of cerebral glycine levels in this infant with NKH.

• Journal of KIISE:Computing Practices and Letters
• /
• v.8 no.6
• /
• pp.705-716
• /
• 2002

This paper proposes a method which visualizes MRI head data in 3 dimensions with direct volume rendering. Though surface rendering is usually used for MRI data visualization, it has some limits of displaying little speckles because it loses the information of the speckles in the surfaces while acquiring the information. Direct volume rendering has ability of displaying little speckles, but it doesn't treat MRI data because of the data features of MRI. In this paper, we try to visualize MRI head data in 3 dimensions as follows. First, we separate the brain region from the head region of MRI head data, next increase the pixel level of the brain region, then combine the brain region with the increased pixel level and the head region without brain region, last visualizes the combined MRI head data with direct volume rendering. We segment the brain region from head region based on histogram threshold, morphology operations and snakes algorithm. The proposed segmentation method shows 91~95% similarity with a hand segmentation. The method rather clearly visualizes the organs of the head in 3 dimensions.

• Proceedings of the KSMRM Conference
• /
• 2003.10a
• /
• pp.24-24
• /
• 2003

Purpose： It is known that diffusion-weighted MR imaging (DWI) is helpful in the evaluation of malignancy grading in brain tumor. This study was to evaluate the DWls with different b-values of various brain tumors in order to determine optimal b-values on 3T MR unit. Method： On a 3T MR unit, DWls with b-values of 1, 000, 3, 000 and 5, 000 s/mm2 were obtained in 20 patients of pathologically-proven brain tumors (7 metastases, 4 high grade gliomas, 2 Iymphomas, 2 low grade gliomas, 2 germinomas, and one each of germinoma, meningioma, hemangioblastoma and central neurocytoma. The overall image quality, contrast between normal brain parenchyma and tumor and signal intensities of solid and cystic components were comparatively evaluated among DWls with different b-values by visual inspection.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.16 no.11
• /
• pp.3479-3492
• /
• 2022

Longitudinal quantification of brain changes due to development, aging or disease plays an important role in the filed of personalized-medicine applications. However, due to the temporal variability in shape and different imaging equipment and parameters, estimating anatomical changes in longitudinal studies is significantly challenging. In this paper, a longitudinal Magnetic Resonance(MR) brain image segmentation algorithm proposed by combining intensity information and anisotropic smoothness term which contain a spatial smoothness constraint and longitudinal consistent constraint into a variational framework. The minimization of the proposed energy functional is strictly and effectively derived from a fast optimization algorithm. A large number of experimental results show that the proposed method can guarantee segmentation accuracy and longitudinal consistency in both simulated and real longitudinal MR brain images for analysis of anatomical changes over time.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.25 no.7A
• /
• pp.1006-1016
• /
• 2000

This study is to segment white matter, gray matter, and cerebrospinal fluid(CSF) on a brain MR image and to calculate the volume of each. First, after removing the background on a brain MR image, we segmented the whole region of a brain from a skull and a fat layer. Then, we calculated the partial volume of each component, which was present in scanning finite thickness, with the arithmetical analysis of gray value from the internal region of a brain showing the blurring effects on the basis of the MR image forming principle. Calculated partial volumes of white matter, gray matter and CSF were used to determine the threshold for the segmentation of each component on a brain MR image showing the blurring effects. Finally, the volumes of segmented white matter, gray matter, and CSF were calculated. The result of this study can be used as the objective diagnostic method to determine the degree of brain atrophy of patients who have neurodegenerative diseases such as Alzheimer's disease and cerebral palsy.

• Investigative Magnetic Resonance Imaging
• /
• v.1 no.1
• /
• pp.125-129
• /
• 1997

Purpose: The aim of this study was to find correlation between the expression of epidermal growth factor receptor (EGFR) and MR findings in the brain glioma. Materials and Methods: MR features including edema, margin, necrosis, heterogeneity, hemorrhage and contrast enhancement were retrospectively analyzed with preoperative MR images in 41 patients with proven brain gliomas (8 low grade astrocytomas, 12 anaplastic astrocytomas, 21 glioblastoma multiformes). Immunohistochemical study of EGFR was done and their expressions were graded by both stained distribution and intensity. Correlation analysis between the MR features and EGFR expressions was done. Results: Peritumoral edema was correlated with both distribution (r=0.71, p=0.00) and stain intensity (r=0.69, p=0.00) of EGFR expression. Other MR features showed no statistical correlation with EGFR expression. Conclusion: MRI is useful in evaluation of brain glioma, and peritumoral edema is useful finding that suggests EGFR expression as well as malignant histopathologic grade of the tumor.

• Proceedings of the KSMRM Conference
• /
• 2003.10a
• /
• pp.99-99
• /
• 2003

To exhibit our clinical experience of diffusion-weighted (DW) MR imaging for various brain pathologies and to determine its role in characterizing brain pathologies in children. DW images in 177 children (M：F＝96：81, mean age, 4.7 years) with various brain pathologies were retrospectively collected over past 3 years. DW images (b value： 1000 s/mm) were reviewed along with corresponding apparent diffusion coefficient (ADC) maps. Brain pathologies included cystic or solid brain tumor (n = 55), cerebral infarct (n = 32), cerebritis with or without brain abscess (n = 21), metabolic or toxic brain disorder (n = 19), demyelinating disease (n = 16), hypoxic-ischemic encephalopathy (n = 16), intracerebral hemorrhage including traumatic brain lesion (n = 15), and posterior reversible leukoencephalopathy (n = 3). We reviewed whether DW images and ADCmaps contribute to further characterization of brain pathologies by defining a chronological age of lesions, the presence of cytotoxic edema in lesions, and the nature of cystic lesions.