• Journal of Korea Multimedia Society
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• v.19 no.12
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• pp.1960-1969
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• 2016

In this paper, we propose an automatic hierarchical organ segmentation method on abdominal CT images. First, similar atlases are selected using bone-based similarity registration and similarity of liver, kidney, and pancreas area. Second, each abdominal organ is roughly segmented using image-based similarity registration and intensity-based locally weighted voting. Finally, the segmented abdominal organ is refined using mask-based affine registration and intensity-based locally weighted voting. Especially, gallbladder and pancreas are hierarchically refined using location information of neighbor organs such as liver, left kidney and spleen. Our method was tested on a dataset of 12 portal-venous phase CT data. The average DSC of total organs was $90.47{\pm}1.70%$. Our method can be used for patient-specific abdominal organ segmentation for rehearsal of laparoscopic surgery.

• Journal of the Korean Society of Radiology
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• v.17 no.5
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• pp.663-670
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• 2023

In this study, we aimed to perform a comparative evaluation using quantitative factors between a region-growing based segmentation with noise reduction algorithms and a U-Net based segmentation. Initially, we applied median filter, median modified Wiener filter, and fast non-local means algorithm to computed tomography (CT) images, followed by region-growing based segmentation. Additionally, we trained a U-Net based segmentation model to perform segmentation. Subsequently, to compare and evaluate the segmentation performance of cases with noise reduction algorithms and cases with U-Net, we measured root mean square error (RMSE) and peak signal to noise ratio (PSNR), universal quality image index (UQI), and dice similarity coefficient (DSC). The results showed that using U-Net for segmentation yielded the most improved performance. The values of RMSE, PSNR, UQI, and DSC were measured as 0.063, 72.11, 0.841, and 0.982 respectively, which indicated improvements of 1.97, 1.09, 5.30, and 1.99 times compared to noisy images. In conclusion, U-Net proved to be effective in enhancing segmentation performance compared to noise reduction algorithms in CT images.

• Journal of Biomedical Engineering Research
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• v.26 no.3
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• pp.171-176
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• 2005

This paper proposes an automatic liver segmentation method using improved partial histogram threshold (PHT) algorithms. This method removes neighboring abdominal organs regardless of random pixel variation of contrast enhanced CT images. Adaptive multi-modal threshold is first performed to extract a region of interest (ROI). A left PHT (LPHT) algorithm is processed to remove the pancreas, spleen, and left kidney. Then a right PHT (RPHT) algorithm is performed for eliminating the right kidney from the ROI. Finally, binary morphological filtering is processed for removing of unnecessary objects and smoothing of the ROI boundary. Ten CT slices of six patients (60 slices) were selected to evaluate the proposed method. As evaluation measures, an average normalized area and area error rate were used. From the experimental results, the proposed automatic liver segmentation method has strong similarity performance as the MSM by medical Doctor.

• Journal of Korea Multimedia Society
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• v.9 no.12
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• pp.1588-1595
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• 2006

In this paper, we proposed a liver extracting procedure for computer aided liver diagnosis system. Extraction of liver region in an abdominal CT image is difficult due to interferences of other organs. For this reason, liver region is extracted in a region of interest(ROI). ROI is selected by the window which can measure the distribution of Hounsfield Unit(HU) value of liver region in an abdominal CT image. The distribution is measured by an existential probability of HU value of lever region in the window. If the probability of any window is over 50%, the center point of the window would be assigned to ROI. Actually, liver region is not clearly discerned from the adjacent organs like muscle, spleen, and pancreas in an abdominal CT image. Liver region is extracted by the watershed segmentation algorithm which is effective in this situation. Because it is very sensitive to the slight valiance of contrast, it generally produces over segmentation regions. Therefore these regions are required to merge into the significant regions for optimal segmentation. Finally, a liver region can be selected and extracted by prier information based on anatomic information.

• Journal of Biomedical Engineering Research
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• v.25 no.3
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• pp.189-194
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• 2004

Pixel values of contrast enhanced computed tomography (CE-CT) images are randomly changed. Also, the middle liver part has a problem to segregate the liver structure because of similar gray-level values of a pancreas in the abdomen. In this paper, an automatic liver segmentation method using a partial histogram threshold (PHT) algorithm is proposed for overcoming randomness of CE-CT images and removing the pancreas. After histogram transformation, adaptive multi-modal threshold is used to find the range of gray-level values of the liver structure. Also, the PHT algorithm is performed for removing the pancreas. Then, morphological filtering is processed for removing of unnecessary objects and smoothing of the boundary. Four CE-CT slices of eight patients were selected to evaluate the proposed method. As the average of normalized average area of the automatic segmented method II (ASM II) using the PHT and manual segmented method (MSM) are 0.1671 and 0.1711, these two method shows very small differences. Also, the average area error rate between the ASM II and MSM is 6.8339 ％. From the results of experiments, the proposed method has similar performance as the MSM by medical Doctor.

• The Korean Journal of Nuclear Medicine Technology
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• v.18 no.1
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• pp.19-25
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• 2014

Purpose: Integrated PET/MRI has been developed recently has become a lot of help to the point oncologic, neological, cardiological nuclear medicine. By using this PET/MRI, a ${\mu}-map$ is created some special MRI sequence which may be divided parts of the body for attenuation correction. However, because an MRI contrast agent is necessary in order to obtain an more MRI information, we will evaluate to see an effect of SUV on PET image that corrected attenuation by MRI with contrast agent. Materials and Methods: As PET/MRI machine, Biograph mMR (Siemens, Germany) was used. For phantom test, 1mCi $^{18}F-FDG$ was injected in cylinderical uniformity phantom, and then acquire PET data about 10 minutes with VIBE-DIXON, UTE MRI sequence image for attenuation correction. T1 weighted contrast media, 4 cc DOTAREM (GUERBET, FRANCE) was injected in a same phatnom, and then PET data, MRI data were acquired by same methodes. Using this PET, non-contrast MRI and contrast MRI, it was reconstructed attenuation correction PET image, in which we evanuated the difference of SUVs. Additionally, for let a high desity of contrast media, 500 cc 2 plastic bottles were used. We injected $^{18}F-FDG$ with 5 cc DOTAREM in first bottle. At second bottle, only $^{18}F-FDG$ was injected. and then we evaluated a SUVs reconstructed by same methods. For clinical patient study, rectal caner-pancreas cancer patients were selected. we evaluated SUVs of PET image corrected attenuastion by contrast weighted MRI and non-contrast MRI. Results: For a phantom study, although VIBE DIXON MRI signal with contrast media is 433% higher than non-contrast media MRI, the signals intensity of ${\mu}-map$, attenuation corrected PET are same together. In case of high contrast media density, image distortion is appeared on ${\mu}-map$ and PET images. For clinical a patient study, VIBE DIXON MRI signal on lesion portion is increased in 495% by using DOTAREM. But there are no significant differences at ${\mu}-map$, non AC PET, AC-PET image whether using contrast media or not. In case of whole body PET/MRI study, %diff between contras and non contrast MRAC at lung, liver, renal cortex, femoral head, myocardium, bladder, muscle are -4.32%, -2.48%, -8.05%, -3.14%, 2.30%, 1.53%, 6.49% at each other. Conclusion: In integrated PET/MRI, a segmentation ${\mu}-map$ method is used for correcting attenuation of PET signal. although MRI signal for attenuation correciton change by using contrast media, ${\mu}-map$ will not change, and then MRAC PET signal will not change too. Therefore, MRI contrast media dose not affect for attenuation correction PET. As well, not only When we make a flow of PET/MRI protocol, order of PET and MRI sequence dose not matter, but It's possible to compare PET images before and after contrast agent injection.