• Transactions on Electrical and Electronic Materials
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• v.15 no.4
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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.

• Journal of radiological science and technology
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• v.31 no.4
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• pp.355-365
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• 2008

Purpose : This study isto determine the grade of brain tumor and compare the characteristics in each grade using in MRS (MR Spectroscopy). Method : STEAM (Stimulated Echo Acquisition Method) and protocol of PRESS (Point Resolved Spectroscopy) were used in the levels of tumor grade. We classified the pattern of tumor and analysis of the spectrum signals quantitatively from voxel in the brain tumor grade. In accordance with the result, we calculated the accuracy of biochemical. Result : In high-grade tumor, the NAA/Cr showed the signal reduction of 29.4% and 53.9%. However Cho/Cr increased 570% and 711%. However, in low-grade tumor, NAA/Cr downed to 42.6% and 58.1%. Cho/Cr increased to 188% and 195%. Conclusion : The study suggests that the comparative analysis of signals from MR spectroscopy could be useful to evaluate the grade of tumor and find out the characteristics of it. By extension, MR spectroscopy can be used for research with other organs in the human.

• Proceedings of the KSMRM Conference
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• 2003.10a
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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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• v.15 no.2
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• pp.102-109
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• 2011

Purpose : To retrospectively evaluate the usefulness of diffusion-weighted images, ADC maps and conventional MR images for determination of brain tumor consistency. Materials and Methods : Twenty-three patients with brain tumor underwent MR examinations with T1, T2 and diffusion-weighted images. Regions of interest (ROIs) were drawn in the tumors, and the measured signal intensities (SI) were normalized with the contralateral side. We evaluated the correlation between SI ratios from various images and tumor consistency assessed at surgery. In three patients with both cystic and solid components, each component was evaluated independently. Qualitatively observed SIs were also correlated with tumor consistency. Results : Statistical analysis revealed significant correlation between tumor consistency and ADC ratio (r = -0.586, p = 0.002), SI ratios on T2-weighted images (r = -0.497, p = 0.010), and observed SIs on T2-weighted images (r = -0.461, p = 0.018). The relative ratio of ADC value correlated with tumor consistency most strongly. Conclusion : The measured ratio of ADC, SI ratio and observed SI grade on T2-weighted images can provide valuable information about the consistency of brain tumor.

• Investigative Magnetic Resonance Imaging
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• v.1 no.1
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• pp.125-129
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• 1997

Purpose: The aim of this study was to find correlation between the expression of epidermal growth factor receptor (EGFR) and MR findings in the brain glioma. Materials and Methods: MR features including edema, margin, necrosis, heterogeneity, hemorrhage and contrast enhancement were retrospectively analyzed with preoperative MR images in 41 patients with proven brain gliomas (8 low grade astrocytomas, 12 anaplastic astrocytomas, 21 glioblastoma multiformes). Immunohistochemical study of EGFR was done and their expressions were graded by both stained distribution and intensity. Correlation analysis between the MR features and EGFR expressions was done. Results: Peritumoral edema was correlated with both distribution (r=0.71, p=0.00) and stain intensity (r=0.69, p=0.00) of EGFR expression. Other MR features showed no statistical correlation with EGFR expression. Conclusion: MRI is useful in evaluation of brain glioma, and peritumoral edema is useful finding that suggests EGFR expression as well as malignant histopathologic grade of the tumor.

• Journal of Korea Multimedia Society
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• v.18 no.10
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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.

• Proceedings of the Korean Society of Medical Physics Conference
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• 2003.09a
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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.

• Proceedings of the Korean Society of Medical Physics Conference
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• 2002.09a
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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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• v.22 no.4
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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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• v.8 no.2
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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.