• The KIPS Transactions:PartA
• /
• v.9A no.1
• /
• pp.83-92
• /
• 2002

The segmentation of target objects from three dimensional volume images is an essential step for visualization and volume measurement. In this paper, we present a method to detect the surface of objects by improving the widely used levoy filtering for volume visualization. Using morphological operators we generate completely closed surfaces and selectively segment objects using the volume growing algorithm. The presented method was applied to 3-D artificial sphere images and angiocardiograms. We quantitatively compared this method with the conventional levoy filtering using artificial sphereimages, and the results showed that our method is better in the aspect of voxel errors. The results of visual comparison using angiocardiograms also showed that our method is more accurate. The presented method in this paper is very effective for segmentation of volume data because segmentation, visualization and measurement are frequently used together for 3-D image processing and they can be easily related in our method.

• Journal of the Korea Computer Graphics Society
• /
• v.10 no.4
• /
• pp.27-36
• /
• 2004

본 논문에서는 뇌의 하부구조인 해마를 정확하게 분석하기 위한 형상 정규화 방법과 정상인과 간질 환자의 해마를 분류하기 위한 방법을 제시한다. 해마에 대한 형상 분석 과정은 크게 형상 표현을 구축하는 과정, 형상의 유사도를 측정하는 과정, 정상인 집단과 환자 집단을 분류하는 과정으로 이루어진다. 본 연구에서는 해마의 형상 표현으로 메쉬, 골격, 복셀로 이루어진 하이브리드 옥트리 자료구조를 구축하였다. 또한 Iterative Closest Point (ICP) 알고리즘을 사용하여 해마 골격을 기반으로 한 정규화를 수행하였다. 그리고 정규화된 해마 형상을 전역적, 국부적으로 분석하여 최종적으로 입력된 해마가 정상인 또는 간질 환자에 속하는지를 학습된 데이터를 이용하여 분류하였다. 본 논문에서 제시한 ICP 기반의 정규화 방법은 3차원 해마 형상을 정확하게 분석하게 해주고, 골격의 정점 수를 조절함으로써 정규화 시간을 감소시킬 수 있다. 뿐만 아니라 3차원 해마 모델의 형상을 신경망을 통하여 학습시킴으로써 해마의 형상이 변형된 환자 집단과 정상인 집단을 분류하는데 이용할 수 있다.

• Journal of KIISE:Computer Systems and Theory
• /
• v.27 no.8
• /
• pp.691-700
• /
• 2000

The standard volume ray-tracing, optimized with octree, needs to repeatedly traverse hierarchical structures for each ray that often leads to redundant computations. It also employs the expensive 3D interpolation for producing high quality images. In this paper, we present a new ray-casting method that efficiently computes shaded colors and opacities at resampling points by traversing octree only once. This method traverses volume data in object-order, finds resampling points on slices incrementally, and performs resampling based on 2D interpolation. While the early ray-termination, which is one of the most effective optimization techniques, is not easily combined with object-order methods, we solved this problem using a dynamic data structure in image space. Considering that our new method is easy to implement, and need little additional memory, it will be used as very effective volume method that fills the performance gap between ray-casting and shear-warping.

• The Transactions of the Korea Information Processing Society
• /
• v.6 no.4
• /
• pp.1081-1089
• /
• 1999

Volume data modeling is concerned with finding a mathematical function which represents the relationship implied by the 3D data. Modeling a volume data geometrically can visualize a volume data using surface graphics without voxelization. It has many merits in that it is fast and requires little memory. We proposes, a method based on wavelet transformation and tetrahedrization. we implement a prototype system based on the proposed method. Last, we evaluated the proposed method comparing it with marching cube algorithm. the evaluation results show that though the proposed method uses only 13% of the volume data, the images generated is as good as the images generated by the marching cubes algorithm.

• Journal of Biomedical Engineering Research
• /
• v.38 no.3
• /
• pp.116-122
• /
• 2017

Physical breast phantoms would be useful for the development of a dedicated breast computed tomography (BCT) system and its optimization. While the conventional breast phantoms are available in compressed forms, which are appropriate for the mammography and digital tomosynthesis, however, the BCT requires phantoms in uncompressed forms. Although simple cylindrical plastic phantoms can be used for the development of the BCT system, they will not replace the roles of uncompressed phantoms describing breast anatomies for a better study of the BCT. In this study, we have designed a numerical voxel breast phantom accounting for the random nature of breast anatomies and applied it to the 3D printer to fabricate the uncompressed anthropomorphic breast phantom. The numerical voxel phantom mainly consists of the external skin and internal anatomies, including the ductal networks, the glandular tissues, the Cooper's ligaments, and the adipose tissues. The voxel phantom is then converted into a surface data in the STL file format by using the marching cube algorithm. Using the STL file, we obtain the skin and the glandular tissue from the 3D printer, and then assemble them. The uncompressed breast phantom is completed by filling the remaining space with oil, which mimics the adipose tissues. Since the breast phantom developed in this study is completely software-generated, we can create readily anthropomorphic phantoms accounting for diverse human breast anatomies.

• Journal of KIISE:Computer Systems and Theory
• /
• v.27 no.3
• /
• pp.235-244
• /
• 2000

This paper addresses a new method for constructing surface representation of 3D structures from a sequence of tomographic cross-sectional images, Firstly, we propose cell-boundary representation by transforming the cuberille space into cell space. A cell-boundary representation consists of a set of boundary cells with their 1-voxel configurations, and can compactly describe binary volumetric data. Secondly, to produce external surface from the cell-boundary representation, we define 19 modeling primitives (MP) including volumetric, planar and linear groups. Surface polygons are created from those modeling primitives using a simple table look-up operation. Comparing with previous method such as Marching Cube or PVP algorithm, our method is robust and does not make any crack in resulting surface model. Hardware implementation is expected to be easy because our algorithm is simple(scan-line), efficient and guarantees data locality in computation time.

• Investigative Magnetic Resonance Imaging
• /
• v.17 no.4
• /
• pp.294-307
• /
• 2013

Purpose : To investigate the correlations between Seoul Neuropsychological Screening Battery (SNSB) scores and the gray matter volumes (GMV) in patients with Alzheimer's disease (AD) and mild cognitive impairment (MCI) and cognitively normal (CN) elderly subjects with correcting the genotypes. Materials and Methods: Total 75 subjects were enrolled with 25 subjects for each group. The apolipoprotein E (APOE) epsilon genotypes, SNSB scores, and the 3D T1-weighted images were obtained from all subjects. Correlations between SNSB scores and GMV were investigated with the multiple regression method for each subject group using both voxel-based and region-of-interest-based analyses with covariates of age, gender, and the genotype. Results: In the AD group, Rey Complex Figure Test (RCFT) delayed recall scores were positively correlated with GMV. In the MCI group, Seoul Verbal Learning Test (SVLT) scores were positively correlated with GMV. In the CN group, GMV negatively correlated with Boston Naming Test (K-BNT) scores and Mini-Mental State Examimation (K-MMSE) scores, but positively correlated with RCFT scores. Conclusion: When we used covariates of age, gender, and the genotype, we found statistically significant correlations between some SNSB scores and GMV at some brain regions. It may be necessary to further investigate a longitudinal study to understand the correlation.

• The Korean Journal of Nuclear Medicine Technology
• /
• v.16 no.1
• /
• pp.12-16
• /
• 2012

Purpose : In conventional PET image reconstruction, iterative reconstruction methods such as OSEM (Ordered Subsets Expectation Maximization) have now generally replaced traditional analytic methods such as filtered back-projection. This includes improvements in components of the system model geometry, fully 3D scatter and low noise randoms estimates. SharpIR algorithm is to improve PET image contrast to noise by incorporating information about the PET detector response into the 3D iterative reconstruction algorithm. The aim of this study is evaluation of SharpIR reconstruction method in PET/CT. Materials and Methods: For the measurement of detector response for the spatial resolution, a capillary tube was filled with FDG and scanned at varying distances from the iso-center (5, 10, 15, 20 cm). To measure image quality for contrast recovery, the NEMA IEC body phantom (Data Spectrum Corporation, Hillsborough, NC) with diameters of 1, 13, 17 and 22 for simulating hot and 28 and 37 mm for simulating cold lesions. A solution of 5.4 kBq/mL of $^{18}F$-FDG in water was used as a radioactive background obtaining a lesion of background ratio of 4.0. Images were reconstructed with VUE point HD and VUE point HD using SharpIR reconstruction algorithm. For the clinical evaluation, a whole body FDG scan acquired and to demonstrate contrast recovery, ROIs were drawn on a metabolic hot spot and also on a uniform region of the liver. Images were reconstructed with function of varying iteration number (1~10). Results: The result of increases axial distance from iso-center, full width at half maximum (FWHM) is also increasing in VUE point HD reconstruction image. Even showed an increasing distances constant FWHM. VUE point HD with SharpIR than VUE point HD showed improves contrast recovery in phantom and clinical study. Conclusion: By incorporating more information about the detector system response, the SharpIR algorithm improves the accuracy of underlying model used in VUE point HD. SharpIR algorithm improve spatial resolution for a line source in air, and improves contrast recovery at equivalent noise levels in phantoms and clinical studies. Therefore, SharpIR algorithm can be applied as through a longitudinal study will be useful in clinical.

• Investigative Magnetic Resonance Imaging
• /
• v.17 no.4
• /
• pp.275-285
• /
• 2013

Purpose : The objective of this study was to investigate effects of different smoothing kernel sizes on brain tissue-masked susceptibility-weighted images (SWI) obtained from normal elderly subjects using voxel-based analyses. Materials and Methods: Twenty healthy human volunteers (mean $age{\pm}SD$ = $67.8{\pm}6.09$ years, 14 females and 6 males) were studied after informed consent. A fully first-order flow-compensated three-dimensional (3D) gradient-echo sequence ran to obtain axial magnitude and phase images to generate SWI data. In addition, sagittal 3D T1-weighted images were acquired with the magnetization-prepared rapid acquisition of gradient-echo sequence for brain tissue segmentation and imaging registration. Both paramagnetically (PSWI) and diamagnetically (NSWI) phase-masked SWI data were obtained with masking out non-brain tissues. Finally, both tissue-masked PSWI and NSWI data were smoothed using different smoothing kernel sizes that were isotropic 0, 2, 4, and 8 mm Gaussian kernels. The voxel-based comparisons were performed using a paired t-test between PSWI and NSWI for each smoothing kernel size. Results: The significance of comparisons increased with increasing smoothing kernel sizes. Signals from NSWI were greater than those from PSWI. The smoothing kernel size of four was optimal to use voxel-based comparisons. The bilaterally different areas were found on multiple brain regions. Conclusion: The paramagnetic (positive) phase mask led to reduce signals from high susceptibility areas. To minimize partial volume effects and contributions of large vessels, the voxel-based analysis on SWI with masked non-brain components should be utilized.