• Transactions on Electrical and Electronic Materials
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• 제15권4호
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• pp.230-234
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• 2014

Automatic detection of disease helps medical institutions that are introducing digital images to read images rapidly and accurately, and is thus applicable to lesion diagnosis and treatment. The aim of this study was to apply a symmetry contribution algorithm to unsharp mask filter-applied MR images and propose an analysis technique to automatically recognize brain tumor and edema. We extracted the skull region and drawed outline of the skull in database of images obtained at P University Hospital and detected an axis of symmetry with cerebral characteristics. A symmetry contribution algorithm was then applied to the images around the axis of symmetry to observe intensity changes in pixels and detect disease areas. When we did not use the unsharp mask filter, a brain tumor was detected in 60 of a total of 95 MR images. The disease detection rate for the brain was 63.16%. However, when we used the unsharp mask filter, the tumor was detected in 87 of a total of 95 MR images, with a disease detection rate of 91.58%. When the unsharp mask filter was used in the pre-process stage, the disease detection rate for the brain was higher than when it was not used. We confirmed that unsharp mask filter can be used to rapidly and accurately to read many MR images stored in a database.

• 대한방사선기술학회지:방사선기술과학
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• 제31권4호
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• pp.355-365
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• 2008

대뇌 종양의 질환에서 Tumor의 정도를 M.R.S(MR Spectroscopy)를 이용하여 질환의 등급별 비교 분석과 특성을 연구하고자 하였다. M.R.S의 STEAM과 PRESS의 Protocol를 이용하여 High grade Tumor와 Low grade Tumor의 성분을 절편화 하고 질환별 Voxel의 Spectrum의 신호를 얻어서 질환의 생화학적 정보를 정량 분석하고, 각각 실험 결과에 대하여 비교 분석하여 질환의 성분 분석과 질환의 유형을 이해하고자 하였다. High grade Tumor에서는 NAA/Cr이 29.4%와 53.9%의 신호 감소를 보였고, Cho/Cr은 570%와 711%의 급격한 신호 증가를 보임을 알 수 있었고, Low grade Tumor에서는 NAA/Cr이 42.6%와 58.1%의 신호 감소를 보였고, Cho/Cr은 188%와 195%의 신호 증가를 보임을 알 수 있었다. 그리하여, 각 신호의 비교분석을 통한 종양의 정도와 특성을 이해할 수 있었고, 여러 인체장기에 폭 넓은 연구와 자료 확립을 하여야 할 것이다.

• 대한자기공명의과학회:학술대회논문집
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• 대한자기공명의과학회 2003년도 제8차 학술대회 초록집
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• pp.24-24
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• 2003

Purpose： It is known that diffusion-weighted MR imaging (DWI) is helpful in the evaluation of malignancy grading in brain tumor. This study was to evaluate the DWls with different b-values of various brain tumors in order to determine optimal b-values on 3T MR unit. Method： On a 3T MR unit, DWls with b-values of 1, 000, 3, 000 and 5, 000 s/mm2 were obtained in 20 patients of pathologically-proven brain tumors (7 metastases, 4 high grade gliomas, 2 Iymphomas, 2 low grade gliomas, 2 germinomas, and one each of germinoma, meningioma, hemangioblastoma and central neurocytoma. The overall image quality, contrast between normal brain parenchyma and tumor and signal intensities of solid and cystic components were comparatively evaluated among DWls with different b-values by visual inspection.

• Investigative Magnetic Resonance Imaging
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• 제15권2호
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• pp.102-109
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• 2011

목적 : 수술 전 축내 뇌종양의 경도를 평가하는데 확산강조영상 및 현성확산계수영상과 고식적 MR 영상의 유용성을 알아보고자 하였다. 대상 및 방법: 축내 뇌종양으로 수술을 받은 23명의 환자를 대상으로 T1, T2, 확산강조영상 소견을 후향적으로 분석하였다. 다양한 MR영상에서의 신호강도와 수술에서 평가한 종양의 경도를 정량적, 정성적으로 비교 분석하였다. 수술소견에서 종양의 경도는 낭성, 젤리같은, 쉽게 부서지는, 부드러운, 단단한, 딱딱한 정도로 나눴다. 세 명의 환자에서는 낭성 부분과 고형성 부분이 함께 있어서 각각에 대해서 평가하였다. 결과: 종양이 단단할수록 현성확산계수와 T2강조영상에서의 신호강도의 비는 더 낮았다 (p = 0.002, p = 0.01). 종양의 경도와 현성확산계수가 가장 강한 선형상관관계를 보였다 (r = -0.586, p = 0.002). 정성적 분석에서는 단단할수록 T2강조영상에서 정성적 신호강도 등급이 낮았다 (p = 0.018). 그 외 다른 MR소견은 통계분석에서 종 양의 경도와 유의한 상관관계를 보이지 않았다. 결론: 축내 뇌종양의 현성확산계수, T2강조영상에서 신호강도의 비와 정성적 신호강도 등급은 종양의 경도와 상관관계가 있었고 이는 수술 전 전략 수립에 많은 도움이 될 것으로 생각된다.

• Investigative Magnetic Resonance Imaging
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• 제1권1호
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• pp.125-129
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• 1997

목적: 신경고종의 자기공명영상 소견과 표피성장인자수용체의 발현 사이의 상관관계를 알아보고자 본 실험을 시행하였다. 대상 및 방법: 수술적 제거 혹은 생검으로 확진된 41예의 신경교종(저등급 성상세포종 8예, 역형성 성상세포종 12예, 다형성 교아세포종 21예) 환자에서 시행한 자기공명영상을 종양의 경계, 괴사, 균질도, 출혈, 조영증강, 그리고 종괴주위의 부종에 대하여 분석하여 각 소장인자수용체의 염색을 시행한 후 그 염색 분포 및 강도에 대한 등급값을 정하였다. 각 환자에서 얻어진 종양 조직을 면역조직화학법으로 표피성 장인자수용체의 염색을 시행한 후 그 염색 분포 및 강도에 대해 등급값을 정했다. 각 자기공명소견과 표피성장인자수용체 발현의 상관관계를 통계적으로 알아보았다. 결과: 자기공명영상에서 종괴주위 부종만이 표피성장인자수용체의 염색 분포(r=0.71, p=0.00) 및 염색 강도(r=0.69, p=0.00)와 유의한 상관관계를 보이는 소견이었다. 나머지 소견은 표피성장인자수용체의 발현과 통게적으로 유의한 상관관계를 보이지 않았다. 결론: 자기공명영상은 신경교종의 진단에 있어 유용한 방법이며, 신경교종에서 종괴주위 부종은 그 조직병리학적 악성도의 예측 뿐만 아니라, 표피성장인자수용체의 발현을 예측하는 데에도 도움이 되는 소견이다.

• 한국멀티미디어학회논문지
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• 제18권10호
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• pp.1189-1196
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• 2015

In this paper, we proposed automatic extraction of brain tumor using morphological operation and statistical tumors size in MR images. Neurosurgery have used gamma-knife therapy by MR images. However, the gamma-knife plan systems needs the brain tumor regions, because gamma-ray should intensively radiate to the brain tumor except for normal cells. Therefore, gamma-knife plan systems spend too much time on designating the tumor regions. In order to reduce the time of designation of tumors, we progress the automatical extraction of tumors using proposed method. The proposed method consist of two steps. First, the information of skull at MRI slices remove using statistical tumors size. Second, the ROI is extracted by tumor feature and average of tumors size. The detection of tumor is progressed using proposed and threshold method. Moreover, in order to compare the effeminacy of proposed method, we compared snap-shot and results of proposed method.

• 한국의학물리학회:학술대회논문집
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• 한국의학물리학회 2003년도 제27회 추계학술대회
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• pp.76-76
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• 2003

Purpose: To assess clinical proton MR spectroscopy (MRS) as a noninvasive method for evaluating tumor malignancy at 3T high field system. Methods: Using 3T MRI/MRS system, localized water-suppressed single-voxel technique in patients with brain tumors was employed to evaluate spectra with peaks of N-acetyl aspartate (NAA), choline-containing compounds (Cho), creatine/phosphocreatine (Cr) and lactate. On the basis of Cr, these peak areas were quantificated as a relative ratio. Results: The variation of metabolites measurements of the designated region in 10 normal volunteers was less than 10%. Normal ranges of NAA/Cr and Cho/Cr ratios were 1.67$\pm$018 and 1.16:1:0.15, respectively. NAA/Cr ratio of all tumor tissues was significantly lower than that of the normal tissues (P=0.005). Cho/Cr ratio of high-grade gliomas was significantly higher than that of low-grade gliomas (P= 0.001), Except 4 menigiomas, lactate signal was observed in all tumor cases. Conclusions: The present study demonstrated that the neuronal degradation or loss was observed in all tumor tissues. Higher grade of brain tumors was correlated with higher Cho/Cr ratio, indicating a significant dependence of Cho levels on malignancy of gliomas. This results suggest that clinical proton MR spectroscopy could be useful to predict tumor malignancy. Acknowledgement: This study was supported by a grant of the Mid and Long Term Nuclear R/D Plan Program, Ministry of Science and Technology, Republic of Korea.

• 한국의학물리학회:학술대회논문집
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• 한국의학물리학회 2002년도 Proceedings
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• pp.345-351
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• 2002

The purpose of this study was to assess clinical proton MR spectroscopy (MRS) as a noninvasive method for evaluating brain tumor malignancy at 3T high field system. Using 3T MRI/MRS system, localized water-suppressed single-voxe1 technique in patients with brain tumors was employed to evaluate spectra with peaks of N-acetyl aspartate (NAA), choline-containing compounds (Cho), creatine/phosphocreatine (Cr) and lactate. On the basis of Cr, these peak areas were quantificated as a relative ratio. The variation of metabolites measurements of the designated region in 10 normal volunteers was less than 10%. Normal ranges of NAA/Cr and Cho/Cr ratios were 1.67${\pm}$018 and 1.16${\pm}$0.15, respectively. NAA/Cr ratio of all tumor tissues was significantly lower than that of the normal tissues (p=0.005), but Cho/Cr ratio of all tumor tissue was significantly higher (p=0.001). Cho/Cr ratio of high-grade gliomas was significantly higher than that of low-grade gliomas (P=0.001). Except 4 menigiomas, lactate signal was observed in all tumor cases. The present study demonstrated that the neuronal degradation or loss was observed in all tumor tissues. Higher grade of brain tumors was correlated with higher Cho/Cr ratio, indicating a significant dependence of Cho levels on malignancy of gliomas. Our results suggest that clinical proton MR spectroscopy could be useful to predict tumor malignancy.

• International Journal of Computer Science & Network Security
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• 제22권4호
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• pp.101-110
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• 2022

Histopathological analysis of biopsy specimens is still used for diagnosis and classifying the brain tumors today. The available procedures are intrusive, time consuming, and inclined to human error. To overcome these disadvantages, need of implementing a fully automated deep learning-based model to classify brain tumor into multiple classes. The proposed CNN model with an accuracy of 92.98 % for categorizing tumors into five classes such as normal tumor, glioma tumor, meningioma tumor, pituitary tumor, and metastatic tumor. Using the grid search optimization approach, all of the critical hyper parameters of suggested CNN framework were instantly assigned. Alex Net, Inception v3, Res Net -50, VGG -16, and Google - Net are all examples of cutting-edge CNN models that are compared to the suggested CNN model. Using huge, publicly available clinical datasets, satisfactory classification results were produced. Physicians and radiologists can use the suggested CNN model to confirm their first screening for brain tumor Multi-classification.

• Journal of Multimedia Information System
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• 제8권2호
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• pp.79-84
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• 2021

Detecting brain tumors of different sizes is a challenging task. This study aimed to identify brain tumors using detection algorithms. Most studies in this area use segmentation; however, we utilized detection owing to its advantages. Data were obtained from 64 patients and 11,200 MR images. The deep learning model used was RetinaNet, which is based on ResNet152. The model learned three different types of pre-processing images: normal, general histogram equalization, and contrast-limited adaptive histogram equalization (CLAHE). The three types of images were compared to determine the pre-processing technique that exhibits the best performance in the deep learning algorithms. During pre-processing, we converted the MR images from DICOM to JPG format. Additionally, we regulated the window level and width. The model compared the pre-processed images to determine which images showed adequate performance; CLAHE showed the best performance, with a sensitivity of 81.79%. The RetinaNet model for detecting brain tumors through deep learning algorithms demonstrated satisfactory performance in finding lesions. In future, we plan to develop a new model for improving the detection performance using well-processed data. This study lays the groundwork for future detection technologies that can help doctors find lesions more easily in clinical tasks.