• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.8
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• pp.500-505
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• 2003

In this paper, the method to register multiple sets of skull CT images to absolute coordinate system is proposed. Contrary to correspondence paired mapping of previous techniques, four anatomical landmark points, three coplanar points and one non-coplanar point, compose three principal axes simple and unique for efficient registration by means of rigid body transformation. Throughout the numerical simulation with added random noises, the error performances in terms of different rotation and rounding-off of landmark points, and incorrect localization of anatomical landmark and target points are quantitatively analyzed to generalize the proposed technique. Experiments using real skull CT images demonstrate the feasibility for an efficient use in clinical practice.

• Journal of Korea Multimedia Society
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• v.17 no.10
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• pp.1205-1212
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• 2014

In this paper, we propose a high-quality stitching method of 3D multiple dental CT images. First, a weighted function is generated using the difference of two distance functions that calculate a distance from the nearest edge of an overlapped region to each position. And a blending ratio propagation function for two gradient vectors is parameterized by the difference and magnitude of gradient vectors that is also applied by the weighted function. When the blending ratio is propagated, an improved region growing scheme is proposed to decide the next position and calculate the blending intensity. The proposed method produces a high-quality stitching image. Our method removes the seam artifact caused by the mean intensity difference between images and vignetting effect. And it removes double edges caused by local misalignment. Experimental results showed that the proposed method produced high-quality stitching images for ten patients. Our stitching method could be usefully applied into the stitching of 3D or 2D multiple images.

• Smart Media Journal
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• v.9 no.3
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• pp.59-70
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• 2020

This paper presents a fully automatic tool to recognize the liver region from CT images based on a deep learning model, namely Multiple Filter U-net, MFUnet. The advantages of both U-net and Multiple Filters were utilized to construct an autoencoder model, called MFUnet for segmenting the liver region from computed tomograph. The MFUnet architecture includes the autoencoding model which is used for regenerating the liver region, the backbone model for extracting features which is trained on ImageNet, and the predicting model used for liver segmentation. The LiTS dataset and Chaos dataset were used for the evaluation of our research. This result shows that the integration of Multiple Filter to U-net improves the performance of liver segmentation and it opens up many research directions in medical imaging processing field.

• Nuclear Medicine and Molecular Imaging
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• v.42 no.1
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• pp.81-81
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• 2008

A 69-year old male with cholangiocellular carcinoma (CCC) was assigned to our department for whole body PET/CT scan. $^{18}F$-FDG PET/CT images showed an intense hypermetabolic lobulating mass(SUVmax = 8.7 / size : 11.4 mm) in the right hepatic lobe with multiple metastatic lung nodules. We made three dimensional volume rendering fusion images by using advantage workstation 4.3 (GE health care) which provide quick anatomic overview and improve the planning process significantly.

• Nuclear Medicine and Molecular Imaging
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• v.42 no.2
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• pp.172-180
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• 2008

PET/CT fused image with anatomical and functional information have improved medical diagnosis and interpretation. This fusion has resulted in more precise localization and characterization of sites of radio-tracer uptake. However, a motion during whole-body imaging has been recognized as a source of image quality degradation and reduced the quantitative accuracy of PET/CT study. The respiratory motion problem is more challenging in combined PET/CT imaging. In combined PET/CT, CT is used to localize tumors and to correct for attenuation in the PET images. An accurate spatial registration of PET and CT image sets is a prerequisite for accurate diagnosis and SUV measurement. Correcting for the spatial mismatch caused by motion represents a particular challenge for the requisite registration accuracy as a result of differences in PET/CT image. This paper provides a brief summary of the materials and methods involved in multiple investigations of the correction for respiratory motion in PET/CT imaging, with the goal of improving image quality and quantitative accuracy.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.24 no.11
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• pp.30-41
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• 2010

In this study we propose a new texture feature extraction method based on an estimation of the brightness and structural uniformity of CT images representing the important characteristics for emphysema recognition. The Center-Symmetric Local Binary Pattern (CS-LBP) is first used to combine gray level in order to describe the brightness uniformity characteristics of the CT image. Then the gradient orientation difference is proposed to generate another CS-LBP code combining with gray level to represent the structural uniformity characteristics of the CT image. The usage of the gray level, CS-LBP and gradient orientation differences enables the proposed method to extract rich and distinctive information from the CT images in multiple directions. Experimental results showed that the performance of the proposed method is more stable with respect to sensitivity and specificity when compared with the SGLDM, GLRLM and GLDM. The proposed method outperformed these three conventional methods (SGLDM, GLRLM, and GLDM) 7.85[%], 22.87[%], and 16.67[%] respectively, according to the diagnosis of average accuracy, demonstrated by the Receiver Operating Characteristic (ROC) curves.

• Journal of Korean Neurosurgical Society
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• v.56 no.5
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• pp.400-404
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• 2014

Objective : Clip artifacts limit the visualization of intracranial structures in CT scans from patients after aneurysmal clipping with cobalt alloy clips. This study is to analyze the parameters influencing the degree of clip artifacts. Methods : Postoperative CT scans of 60 patients with straight cobalt alloy-clipped aneurysms were analyzed for the maximal diameter of white artifacts and the angle and number of streak artifacts in axial images, and the maximal diameter of artifacts in three-dimensional (3-D) volume-rendered images. The correlation coefficient (CC) was determined between each clip artifact type and the clip blade length and clip orientation to the CT scan (angle a, lateral clip inclination in axial images; angle b, clip gradient to scan plane in lateral scout images). Results : Angle b correlated negatively with white artifacts (r=-0.589, p<0.001) and positively with the angle (r=0.636, p<0.001) and number (r=0.505, p<0.001) of streak artifacts. Artifacts in 3-D images correlated with clip blade length (r=0.454, p=0.004). Multiple linear regression analysis revealed that angle b was the major parameter influencing white artifacts and the angle and number of streak artifacts in axial images (p<0.001), whereas clip blade length was a major factor in 3-D images (p=0.034). Conclusion : Use of a clip orientation perpendicular to the scan gantry angle decreased the amount of white artifacts and allowed better visualization of the clip site.

• Journal of Computing Science and Engineering
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• v.4 no.4
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• pp.368-387
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• 2010

We propose a novel method for the registration of 3D CT scans to 2D endoscopic images during the image-guided medialization laryngoplasty. This study aims to allow the surgeon to find the precise configuration of the implant and place it into the desired location by employing accurate registration methods of the 3D CT data to intra-operative patient and interactive visualization tools for the registered images. In this study, the proposed registration methods enable the surgeon to compare the outcome of the procedure to the pre-planned shape by matching the vocal folds in the CT rendered images to the endoscopic images. The 3D image fusion provides an interactive and intuitive guidance for surgeon by visualizing a combined and correlated relationship of the multiple imaging modalities. The 3D Magic Lens helps to effectively visualize laryngeal anatomical structures by applying different transparencies and transfer functions to the region of interest. The preliminary results of the study demonstrated that the proposed method can be readily extended for image-guided surgery of real patients.

• Journal of Veterinary Science
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• v.23 no.6
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• pp.70.1-70.13
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• 2022

Background: Fat stranding is a non-specific finding of an increased fat attenuation on computed tomography (CT) images. Fat stranding is used for detecting the underlying lesion in humans. Objectives: To assess the clinical significance of fat stranding on CT images for identifying the underlying cause in dogs and cats. Methods: In this retrospective study, the incidence, location, extent, distribution, and pattern of fat stranding were assessed on CT studies obtained from 134 cases. Results: Fat stranding was found in 38% (51/134) of all cases and in 35% (37/107) of tumors, which was significantly higher in malignant tumors (44%) than benign tumors (12%). Moreover, fat stranding was found in more than two areas in malignant tumors (16/33) and in a single area in benign tumors (4/4). In inflammation, fat stranding was demonstrated in 54% (7/13) in a single area (7/7) as a focal distribution (6/7). In trauma, fat stranding was revealed in 50% (7/14) and most were in multiple areas (6/7). Regardless of the etiologies, fat stranding was always around the underlying lesion and a reticular pattern was the most common presentation. Logistic regression analysis revealed that multiple areas (p = 0.040) of fat stranding and a reticulonodular pattern (p = 0.022) are the significant predictors of malignant tumor. Conclusions: These findings indicated that CT fat stranding can be used as a clue for identifying the underlying lesion and can be useful for narrowing the differential list based on the extent and pattern.

• Journal of International Society for Simulation Surgery
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• v.1 no.1
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• pp.37-40
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• 2014

Purpose For living donor liver transplantation, liver segmentation is difficult due to the variability of its shape across patients and similarity of the density of neighbor organs such as heart, stomach, kidney, and spleen. In this paper, we propose an automatic segmentation of the liver using multi-planar anatomy and deformable surface model in portal phase of abdominal contrast-enhanced CT images. Method Our method is composed of four main steps. First, the optimal liver volume is extracted by positional information of pelvis and rib and by separating lungs and heart from CT images. Second, anisotropic diffusing filtering and adaptive thresholding are used to segment the initial liver volume. Third, morphological opening and connected component labeling are applied to multiple planes for removing neighbor organs. Finally, deformable surface model and probability summation map are performed to refine a posterior liver surface and missing left robe in previous step. Results All experimental datasets were acquired on ten living donors using a SIEMENS CT system. Each image had a matrix size of $512{\times}512$ pixels with in-plane resolutions ranging from 0.54 to 0.70 mm. The slice spacing was 2.0 mm and the number of images per scan ranged from 136 to 229. For accuracy evaluation, the average symmetric surface distance (ASD) and the volume overlap error (VE) between automatic segmentation and manual segmentation by two radiologists are calculated. The ASD was $0.26{\pm}0.12mm$ for manual1 versus automatic and $0.24{\pm}0.09mm$ for manual2 versus automatic while that of inter-radiologists was $0.23{\pm}0.05mm$. The VE was $0.86{\pm}0.45%$ for manual1 versus automatic and $0.73{\pm}0.33%$ for manaual2 versus automatic while that of inter-radiologist was $0.76{\pm}0.21%$. Conclusion Our method can be used for the liver volumetry for the pre-surgery planning of living donor liver transplantation.