• Journal of KIISE:Software and Applications
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• v.32 no.7
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• pp.612-624
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• 2005

In this paper, we propose a surface-based registration using a gaussian weighted distance map for PET-CT brain image fusion. Our method is composed of three main steps: the extraction of feature points, the generation of gaussian weighted distance map, and the measure of similarities based on weight. First, we segment head using the inverse region growing and remove noise segmented with head using region growing-based labeling in PET and CT images, respectively. And then, we extract the feature points of the head using sharpening filter. Second, a gaussian weighted distance map is generated from the feature points in CT images. Thus it leads feature points to robustly converge on the optimal location in a large geometrical displacement. Third, weight-based cross-correlation searches for the optimal location using a gaussian weighted distance map of CT images corresponding to the feature points extracted from PET images. In our experiment, we generate software phantom dataset for evaluating accuracy and robustness of our method, and use clinical dataset for computation time and visual inspection. The accuracy test is performed by evaluating root-mean-square-error using arbitrary transformed software phantom dataset. The robustness test is evaluated whether weight-based cross-correlation achieves maximum at optimal location in software phantom dataset with a large geometrical displacement and noise. Experimental results showed that our method gives more accuracy and robust convergence than the conventional surface-based registration.

• Journal of the Korea Society for Simulation
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• v.16 no.3
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• pp.57-63
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• 2007

In real time rendering of fetus the empty space leaping while traversing a ray is most frequently used accelerating technique. The main idea is to skip empty voxel samples which do not contribute the result image and it speeds up the rendering time by avoiding sampling data while traversing a ray in the empty region, saving a substantial number of interpolations. Calculating the distance from the nearest object boundary for every yokel can reduce the sampling operation. Among widely-well-known distance maps, those estimates the true distance, such as euclidean distance, takes a long time to compute because of the complicated floating-point operations, and others which uses approximated distance functions, such as city-block and chessboard, provides faster computation time but sampling error may can occur. In this paper, therefore, we analyze the characteristics of several distance maps and compare the number of samples and rendering time. And we aim to suggest the most appropriate distance map for rendering of fetus in ultrasound image.

• The Korean Journal of Nuclear Medicine
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• v.32 no.5
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• pp.414-424
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• 1998

Purpose: Cross-modality coregistration of positron emission tomography (PET) and magnetic resonance imaging (MR) could enhance the clinical information. In this study we propose a refined technique to improve the robustness of registration, and to implement more realistic visualization of the coregistered images. Materials and Methods: Using the sinogram of PET emission scan, we extracted the robust head boundary and used boundary-enhanced PET to coregister PET with MR. The pixels having 10% of maximum pixel value were considered as the boundary of sinogram. Boundary pixel values were exchanged with maximum value of sinogram. One hundred eighty boundary points were extracted at intervals of about 2 degree using simple threshold method from each slice of MR images. Best affined transformation between the two point sets was performed using least square fitting which should minimize the sum of Euclidean distance between the point sets. We reduced calculation time using pre-defined distance map. Finally we developed an automatic coregistration program using this boundary detection and surface matching technique. We designed a new weighted normalization technique to display the coregistered PET and MR images simultaneously. Results: Using our newly developed method, robust extraction of head boundary was possible and spatial registration was successfully performed. Mean displacement error was less than 2.0 mm. In visualization of coregistered images using weighted normalization method, structures shown in MR image could be realistically represented. Conclusion: Our refined technique could practically enhance the performance of automated three dimensional coregistration.

• Journal of KIISE:Software and Applications
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• v.36 no.12
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• pp.967-976
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• 2009

In this paper, we propose an automatic lower extremity vessel extraction based on rigid registration and bone elimination techniques in CT and CT angiography images. First, automatic partitioning of the lower extremity based on the anatomy is proposed to consider the local movement of the bone. Second, rigid registration based on distance map is performed to estimate the movement of the bone between CT and CT angiography images. Third, bone elimination and vessel masking techniques are proposed to remove bones in CT angiography image and to prevent the vessel near to bone from eroding. Fourth, post-processing based on vessel tracking is proposed to reduce the effect of misalignment and noises like a cartilage. For the evaluation of our method, we performed the visual inspection, accuracy measures and processing time. For visual inspection, the results of applying general subtraction, registered subtraction and proposed method are compared using volume rendering and maximum intensity projection. For accuracy evaluation, intensity distributions of CT angiography image, subtraction based method and proposed method are analyzed. Experimental result shows that bones are accurately eliminated and vessels are robustly extracted without the loss of other structure. The total processing time of thirteen patient datasets was 40 seconds on average.

• The Korean Journal of Nuclear Medicine
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• v.32 no.6
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• pp.490-496
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• 1998

Purpose: We evaluated brain perfusion SPECT findings of MELAS syndrome and mitochondrial myopathy in correlation with MR imaging in search of specific imaging features. Materials and Methods: Subjects were five patients (four females and one male; age range, 1 to 25 year) who presented with repeated stroke-like episodes, seizures or developmental delay or asymptomatic but had elevated lactic acid in CSF and serum. Conventional non-contrast MR imaging and Tc-99m-ethyl cysteinate dimer (ECD) brain perfusion SPECT were Performed and imaging features were analyzed. Results: MRI demonstrated increased T2 signal intensities in the affected areas of gray and white matters mainly in the parietal (4/5) and occipital lobes (4/5) and in the basal ganglia (1/5), which were not restricted to a specific vascular territory. SPECT demonstrated decreased perfusion in the corresponding regions of MRI lesions. In addition, there were perfusion defects in parietal (1 patient), temporal (2), and frontal (1) lobes and basal ganglia (1) and thalami (2). In a patient with mitochondrial myopathy who had normal MRI, decreased perfusion was noted in left parietal area and bilateral thalami. Conclusion: Tc-99m ECD SPECT imaging in patients with MELAS syndrome and mitochondrial myopathy showed hypoperfusion of parieto-occipital cortex, basal ganglia, thalamus and temporal cortex, which were not restricted to a specific vascular territory. There were no specific imaging features on SPECT. The significance of abnormal perfusion on SPECT without corresponding MR abnormalities needs to be evaluated further in larger number of patients.