• Journal of KIISE:Software and Applications
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• v.34 no.1
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• pp.18-29
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• 2007

In this paper we present a system that classifies brain MR images by using 2 level decision tree learning. There are two kinds of information that can be obtained from images. One is the low-level features such as size, color, texture, and contour that can be acquired directly from the raw images, and the other is the high-level features such as existence of certain object, spatial relations between different parts that must be obtained through the interpretation of segmented images. Learning and classification should be performed based on the high-level features to classify images according to their semantic meaning. The proposed system applies decision tree learning to each level separately, and the high-level features are synthesized from the results of low-level classification. The experimental results with a set of brain MR images with tumor are discussed. Several experimental results that show the effectiveness of the proposed system are also presented.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.12
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• pp.1969-1977
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• 2016

We study the connection time of vehicle-to-mobile roadside unit (V2MR) communications which can reduce the significant cost of the fixed RSU by installing a gateway of mobile network into a transit bus called the mobile RSU. In the V2MR communications, the connectivity of a commute vehicle can be improved via ad-hoc connection to a nearby mobile RSU. In this paper, we present a new analysis model to estimate the connection time between a commute vehicle and a mobile RSU, when there is a bus station in the overlapping route. Since the connection time between two vehicles is highly dynamic and unpredictable, our analysis will provide a fundamental basis of connection-time estimation of V2MR communications. Numerical results obtained from VEINS simulation show that our analysis can estimate the connection time of V2MR communications with the average error below 1.0 percent. Moreover, we show that the average connection time of V2MR communications can be extended to approximately 3.85 times of that of V2R communications.

• Journal of Biomedical Engineering Research
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• v.25 no.4
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• pp.243-251
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• 2004

This study was to quantitative analysis compare to dynamic characteristic change of the regional cerebral blood volume (rCBV) after development of cerebral fat embolism in cats using perfusion MR Imaging. Forty-four adult rats were used. Triolein (n = 15), oleic acid (n = 9) and linoleic acid (n = 11) were injected into the internal carotid artery using microcatheter through the transfemoral approach. Polyvinyl alcohol (Ivalon) (n = 9) was injected as a control group. Perfusion MR images were obtained at 30 minutes and 2 hours after embolization, based on T2 and diffusion-weighted images. The data was time-to-signal intensity curve and ΔR$_2$＊ curve were obtained continuously with the aid of home-maid image proc in.leased significantly at 2 hours compared with those of 30 minutes (P＜0.005). In conclusion, cerebral blood flow decreased in cerebral fat embolism immediately after embolization and recovered remarkably in time course. It is thought that clinically informations to dynamic characteristic change of the cerebral hemodynamics to the early finding in cerebral infarction by DWI and PWI

• Journal of the Korean Society of Radiology
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• v.2 no.2
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• pp.31-38
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• 2008

Line scan diffusion weighted imaging (LSDI) pulse sequence for 0.32 T magnetic resonance imaging (MRI) system was developed. In the LSDI pulse sequence, the imaging volume is formed by the intersection of the two perpendicular planes selected by the two slice-selective $\pi$/2-pulse and $\pi$-pulse and two diffusion sensitizing gradients placed on the both side of the refocusing $\pi$-pulse and the standard frequency encoding readout was followed. Since the maximum gradient amplitude for the MR system was 15 mT/m the maximum b value was $301.50s/mm^2$. Using the developed LSDI pulse sequence, the diffusion weighted images for the aqueous NaCl solution phantom and triacylglycerol solution phantom calculated from the line scan diffusion weighted images gives the same results within the standard error range (mean diffusivities = $963.90{\pm}79.83({\times}10^{-6}mm^2/s)$ at 0.32 T, $956.77{\pm}4.12({\times}10^{-6}mm^2/s)$ at 1.5 T) and the LSDI images were insensitive to the magnetic susceptibility difference and chemical shift.

• Journal of the Korean Society of Radiology
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• v.84 no.5
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• pp.1066-1079
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• 2023

Purpose Distinguishing intradural extramedullary (IDEM) spinal ependymoma from myxopapillary ependymoma is challenging due to the location of IDEM spinal ependymoma. This study aimed to investigate the utility of clinical and MR imaging features for differentiating between IDEM spinal and myxopapillary ependymomas. Materials and Methods We compared tumor size, longitudinal/axial location, enhancement degree/pattern, tumor margin, signal intensity (SI) of the tumor on T2-weighted images and T1-weighted image (T1WI), increased cerebrospinal fluid (CSF) SI caudal to the tumor on T1WI, and CSF dissemination of pathologically confirmed 12 IDEM spinal and 10 myxopapillary ependymomas. Furthermore, classification and regression tree (CART) was performed to identify the clinical and MR features for differentiating between IDEM spinal and myxopapillary ependymomas. Results Patients with IDEM spinal ependymomas were older than those with myxopapillary ependymomas (48 years vs. 29.5 years, p < 0.05). A high SI of the tumor on T1W1 was more frequently observed in IDEM spinal ependymomas than in myxopapillary ependymomas (p = 0.02). Conversely, myxopapillary ependymomas show CSF dissemination. Increased CSF SI caudal to the tumor on T1WI was observed more frequently in myxopapillary ependymomas than in IDEM spinal ependymomas (p < 0.05). Dissemination to the CSF space and increased CSF SI caudal to the tumor on T1WI were the most important variables in CART analysis. Conclusion Clinical and radiological variables may help differentiate between IDEM spinal and myxopapillary ependymomas.

• The KIPS Transactions:PartA
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• v.11A no.7 s.91
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• pp.555-562
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• 2004

Both global volume reduction and local shape changes of hippocampus within the brain indicate their abnormal neurological states. Hippocampal shape analysis consists of two main steps. First, construct a hippocampal shape representation model ; second, compute a shape similarity from this representation. This paper proposes a novel method for the analysis of hippocampal shape using integrated Octree-based representation, containing meshes, voxels, and skeletons. First of all, we create multi-level meshes by applying the Marching Cube algorithm to the hippocampal region segmented from MR images. This model is converted to intermediate binary voxel representation. And we extract the 3D skeleton from these voxels using the slice-based skeletonization method. Then, in order to acquire multiresolutional shape representation, we store hierarchically the meshes, voxels, skeletons comprised in nodes of the Octree, and we extract the sample meshes using the ray-tracing based mesh sampling technique. Finally, as a similarity measure between the shapes, we compute $L_2$ Norm and Hausdorff distance for each sam-pled mesh pair by shooting the rays fired from the extracted skeleton. As we use a mouse picking interface for analyzing a local shape inter-actively, we provide an interaction and multiresolution based analysis for the local shape changes. In this paper, our experiment shows that our approach is robust to the rotation and the scale, especially effective to discriminate the changes between local shapes of hippocampus and more-over to increase the speed of analysis without degrading accuracy by using a hierarchical level-of-detail approach.