• Proceedings of the KOSOMBE Conference
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• v.1998 no.11
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• pp.60-61
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• 1998

In this paper, we propose an algorithm for automatic segmentation of 3-dimesional brain MR images. In order to segment 3-dimensional brain MR images, we start segmentation from a mid-sagittal brain MR image. Then the segmented mid-sagittal brain MR image is used as a mask that is applied to the remaining lateral slices. Then we apply preprocessing, which includes thresholding and region-labeling, to the lateral slices, resulting in simplified 3-D brain MR images. Finally, we remove remaining problematic regions in the 3-dimensional brain MR image using the connectivity-based thresholding segmentation algorithm. Experiments show satisfactory results.

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

• The Journal of the Korea Contents Association
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• v.2 no.3
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• pp.1-15
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• 2002

A noel automated brain region extraction method in single channel MR images for visualization and analysis of a human brain is presented. The method generates a volume of brain masks by automatic thresholding using a dual curve fitting technique and by 3D morphological operations. The dual curve fitting can reduce an error in clue fitting to the histogram of MR images. The 3D morphological operations, including erosion, labeling of connected-components, max-feature operation, and dilation, are applied to the cubic volume of masks reconstructed from the thresholded Drain masks. This method can automatically extract a brain region in any displayed type of sequences, including extreme slices, of SPGR, T1-, T2-, and PD-weighted MR image data sets which are not required to contain the entire brain. In the experiments, the algorithm was applied to 20 sets of MR images and showed over 0.97 of similarity index in comparison with manual drawing.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.3
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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.

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

• The Transactions of the Korea Information Processing Society
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• v.7 no.2
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• pp.542-551
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• 2000

In this paper, a novel technique is presented for automatic brain region segmentation in single channel MR image data sets for 3D visualization and analysis. The method detects brain contours in 2D and 3D processing of four steps. The first and the second make a head mask and an initial brain mask by automatic thresholding using a curve fitting technique. The stage 3 reconstructs 3D volume of the initial brain mask by cubic interpolation and generates an intermediate brain mask using morphological operation and labeling of connected components. In the final step, the brain mask is refined by automatic thresholding using curve fitting. This algorithm is useful for fully automatic brain region segmentation of T1-weighted, T2-weighted, PD-weighted, SPGR MRI data sets without considering slice direction and covering a whole volume of a brain. In the experiments, the algorithm was applied to 20 sets of MR images and showed over 0.97 in comparison with manual drawing in similarity index.

• Proceedings of the KSMRM Conference
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• 2002.11a
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• pp.106-106
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• 2002

Purpose： The purposes of our study are (1) to develop a brain phantom which can be used for multimodal image registration, (2) to evaluate the accuracy of image registration with the home-made phantom. Method： A brain phantom which could be used for image registration technique of CT-MR and CT-SPECT images using chamfer matching was developed. The brain phantom was specially designed to obtain imaging dataset of CT, MR, and SPECT. The phantom had an external frame with 4 N-shaped pipes filled with acryl rods for CT, MR imaging and Pb rods for SPECT imaging. 8 acrylic pipes were inserted into the empty space of the brain phantom to be imaged for geometric evaluation of the matching. Accuracy of image fusion was assessed by the comparison between the center points of the section of N-shaped bars in the external frame and the inserted pipes of the phantom. Technique with partially transparent, mixed images using color on gray was used for visual assessment of the image registration process.

• Proceedings of the Korean Society of Medical Physics Conference
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• 2002.09a
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• pp.322-323
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• 2002

Partial volume averaging effect of PET data influences on the accuracy of quantitative measurements of regional brain metabolism because spatial resolution of PET is limited. The purpose of this study was to evaluate the accuracy of partial volume correction carried out on $^{18}$ F-PET images using Hoffman brain phantom. $^{18}$ F-PET Hoffman phantom images were co-registered to MR slices of the same phantom. All the MR slices of the phantom were then segmented to be binary images. Each of these binary images was convolved in 2 dimensions with the spatial resolution of the PET. The original PET images were then divided by the smoothed binary images in slice-by-slice, voxel-by-voxel basis resulting in larger PET image volume in size. This enlarged partial volume corrected PET image volume was multiplied by original binary image volume to exclude extracortical region. The evaluation of partial volume corrected PET image volume was performed by region of interests (ROI) analysis applying ROIs, which were drawn on cortical regions of the original MR image slices, to corrected and original PET image volume. From the ROI analysis, range of regional mean values increases of partial volume corrected PET images was 4 to 14%, and average increase for all the ROIs was about 10% in this phantom study. Hoffman brain phantom study was useful for the objective evaluation of the partial volume correction method. This MR-based correction method would be applicable to patients in the. quantitative analysis of FDG-PET studies.

• Journal of Biomedical Engineering Research
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• v.25 no.1
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• pp.33-41
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• 2004

Accuracy of registration between images acquired from various medical image modalities is one of the critical issues in radiation treatment planing. In this study, a method of accuracy evaluation of image registration using a homemade brain phantom was investigated. Chamfer matching of CT-MR and CT-SPECT imaging was applied for the multimodal image registration. The accuracy of image correlation was evaluated by comparing the center points of the inserted targets of the phantom. The three dimensional root-mean-square translation deviations of the CT-MR and CT-SPECT registration were 2.1${\pm}$0.8 mm and 2.8${\pm}$1.4 mm, respectively. The rotational errors were ＜ 2$^{\circ}$ for the three orthogonal axes. These errors were within a reasonable margin compared with the previous phantom studies. A visual inspection of the superimposed CT-MR and CT- SPECT images also showed good matching results.

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