• 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.

• International Journal of Control, Automation, and Systems
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• 제2권1호
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• pp.100-106
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• 2004

In this paper, we present a novel, rapid approach for the detection of brain tumors and deformity boundaries in medical images using a genetic algorithm with wavelet based preprocessing. The contour detection problem is formulated as an optimization process that seeks the contour of the object in a manner of minimizing an energy function based on an active contour model. The brain tumor segmentation contour, however, cannot be detected in case that a higher gradient intensity exists other than the interested brain tumor and deformities. Our method for discerning brain tumors and deformities from unwanted adjacent tissues is proposed. The proposed method can be used in medical image analysis because the exact contour of the brain tumor and deformities is followed by precise diagnosis of the deformities.

• 대한의용생체공학회:의공학회지
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• 제40권3호
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• pp.105-115
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• 2019

Brain tumor surgery may be difficult, but it is also incredibly important. The technological improvements for traditional brain tumor surgeries have always been a focus to improve the precision of surgery and release the potential of the technology in this important area of the body. The need for precision during brain tumor surgery has led to an increase in Robotic-assisted surgeries (RAS). One of the challenges to the widespread acceptance of RAS in the neurosurgery is to recognize invisible tumor accurately. Therefore, it is important to detect brain tumor size and location because surgeon tries to remove as much tumor as possible. In this paper, we proposed brain tumor detection procedures for MRI (Magnetic Resonance Imaging) system. A method of automatic brain tumor detection is needed to accurately target the location of the lesion during brain tumor surgery and to report the location and size of the lesion. In the qualitative assessment, the proposed method showed better results than those obtained with other brain tumor detection methods. Comparisons among all assessment criteria indicated that the proposed method was significantly superior to the threshold method with respect to all assessment criteria. The proposed method was effective for detecting brain tumor.

• 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.

• 한국방사선학회논문지
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• 제18권6호
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• pp.691-698
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• 2024

뇌종양은 유전적, 환경적, 면역학적, 생화학적 요인을 포함한 다양한 복합적인 요인에서 발생한다. 뇌종양은 원발성과 전이성으로 분류되며, 이들은 발생 원인과 위치에서 차이를 보인다. 뇌종양은 삶의 질에 상당한 영향을 미치며, 종양의 크기와 위치에 따라 두통, 발작, 인지 기능 저하, 운동 기능 장애와 같은 증상이 나타날 수 있다. 뇌종양의 조기 진단은 삶의 질을 향상시키는 데 매우 중요하다. 적시의 발견은 신속한 치료를 가능하게 하여 종양의 성장과 증상의 악화를 예방할 수 있다. 진단 과정은 일반적으로 신경학적 검사, 영상 검사, 조직 검사, 혈액 검사를 포함한다. 특히, MRI는 뇌의 상세한 구조를 고해상도로 제공하여 종양의 위치, 크기, 형태 및 주변 조직을 명확하게 나타낸다. 본 연구에서는 MRI 영상에서 뇌종양을 탐지하고 분할하는 방법을 제안하며, 이를 위해 "BrainTumors_1.0.zip"이라는 이름의 데이터 세트를 구축하였다. 실험 결과는 입력 영상을 필터링함으로써 이미지 품질을 향상시키고 정확한 종양 탐지를 가능하게 함을 보여주었다. 향후 연구는 알고리즘의 일반화, 데이터 세트의 다양화, 자동화된 방법론 개발, 그리고 임상적 유용성을 평가하여 뇌종양 진단과 치료를 위한 도구로 확립하는 것이다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권10호
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• pp.2788-2808
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• 2023

Brain tumors are one of the most threatening malignancies for humans. Misdiagnosis of brain tumors can result in false medical intervention, which ultimately reduces a patient's chance of survival. Manual identification and segmentation of brain tumors from Magnetic Resonance Imaging (MRI) scans can be difficult and error-prone because of the great range of tumor tissues that exist in various individuals and the similarity of normal tissues. To overcome this limitation, the Amended Convolutional Neural Network (ACNN) model has been introduced, a unique combination of three techniques that have not been previously explored for brain tumor detection. The three techniques integrated into the ACNN model are image tissue preprocessing using the Kalman Bucy Smoothing Filter to remove noisy pixels from the input, image tissue segmentation using the Isotonic Regressive Image Tissue Segmentation Process, and feature extraction using the Marr Wavelet Transformation. The extracted features are compared with the testing features using a sigmoid activation function in the output layer. The experimental findings show that the suggested model outperforms existing techniques concerning accuracy, precision, sensitivity, dice score, Jaccard index, specificity, Positive Predictive Value, Hausdorff distance, recall, and F1 score. The proposed ACNN model achieved a maximum accuracy of 98.8%, which is higher than other existing models, according to the experimental results.

• 융합신호처리학회논문지
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• 제12권4호
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• pp.267-273
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• 2011

본 논문은 자기공명 뇌영상을 대상으로 뇌종양 영역을 자동으로 분할하기 위한 방법을 제안한다. 정상적인 뇌영상은 좌우로 대칭인 특징을 지니는 반면에 종양이 존재하는 뇌영상은 종양세포와 부종 및 괴사로 인해 비대칭적인 특징을 가진다. 본 논문에서는 이러한 대칭성을 뇌영상내에 종양영역의 존재 유무를 판별할 수 있는 기준으로 이용한다. 대칭성 분석을 위해서 뇌영역의 윤곽선 정보를 이용해 중심축을 생성하였으며 이는 사전정보를 이용하지 않고 영상의 자체 정보만을 해석해서 중심축을 추출할 수 있다는 점에서 기존의 영상 정합을 통해 해부학적 위치 정보를 추출하고 이를 이용하여 중심축을 찾는 방법과 구별된다. 자기공명 영상에서 정상뇌의 조직은 크게 3가지 클러스터로 분할되며 각 클러스터가 포함하는 영역은 백질과 회백질영역을 포함하는 뇌 실질영역, 뇌척수액(csf)영역, 두개골, 지방 및 뇌막 영역 등으로 나뉜다. 종양이 포함된 영상은 종양과 부종 및 괴사 영역이 추가적으로 존재하며 이는 클러스터링을 이용한 분할을 통해서 구분될 수 있다. 분할된 종양 영역의 중심점은 다음 슬라이스의 종양 영역의 경계를 검출하기 위한 레벨셋 알고리즘에 적용되어 전체 볼륨의 종양 영역의 경계선을 추출하기 위한 초기 시드로 이용된다. 본 논문에서는 3차원 볼륨의 영상(슬라이스)중에서 종양 영역이 존재하는 슬라이스의 종양 영역을 분할하여 이후의 슬라이스에서는 분할작업을 수행하지 않고 영역의 경계선만 추출한다. 자카드 지수와 처리 시간의 비교 분석을 통해 기존의 방법과 비슷한 성능과 빠른 속도로 종양 영역을 분할할 수 있다는 것을 보인다.

• 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.

• Brain Tumor Research and Treatment
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• 제6권2호
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• pp.47-53
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• 2018

Brain tumors represent a diverse spectrum of histology, biology, prognosis, and treatment options. Although MRI remains the gold standard for morphological tumor characterization, positron emission tomography (PET) can play a critical role in evaluating disease status. This article focuses on the use of PET with radiolabeled glucose and amino acid analogs to aid in the diagnosis of tumors and differentiate between recurrent tumors and radiation necrosis. The most widely used tracer is $^{18}F$-fluorodeoxyglucose (FDG). Although the intensity of FDG uptake is clearly associated with tumor grade, the exact role of FDG PET imaging remains debatable. Additionally, high uptake of FDG in normal grey matter limits its use in some low-grade tumors that may not be visualized. Because of their potential to overcome the limitation of FDG PET of brain tumors, $^{11}C$-methionine and $^{18}F$-3,4-dihydroxyphenylalanine (FDOPA) have been proposed. Low accumulation of amino acid tracers in normal brains allows the detection of low-grade gliomas and facilitates more precise tumor delineation. These amino acid tracers have higher sensitivity and specificity for detecting brain tumors and differentiating recurrent tumors from post-therapeutic changes. FDG and amino acid tracers may be complementary, and both may be required for assessment of an individual patient. Additional tracers for brain tumor imaging are currently under development. Combinations of different tracers might provide more in-depth information about tumor characteristics, and current limitations may thus be overcome in the near future. PET with various tracers including FDG, $^{11}C$-methionine, and FDOPA has improved the management of patients with brain tumors. To evaluate the exact value of PET, however, additional prospective large sample studies are needed.

• International Journal of Computer Science & Network Security
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• 제21권2호
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• pp.120-130
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• 2021

The spinal cord or CSF surgery is a very complex process. It requires continuous pre and post-surgery evaluation to have a better ability to diagnose the disease. To detect automatically the suspected areas of tumors and symptoms of CSF leakage during the development of the tumor inside of the brain. We propose a new method based on using computer software that generates statistical results through data gathered during surgeries and operations. We performed statistical computation and data collection through the Google Source for the UK National Cancer Database. The purpose of this study is to address the above problems related to the accuracy of missing hybrid KNN values and finding the distance of tumor in terms of brain cancer or CSF images. This research aims to create a framework that can classify the damaged area of cancer or tumors using high-dimensional image segmentation and Laplace transformation method. A high-dimensional image segmentation method is implemented by software modelling techniques with measures the width, percentage, and size of cells within the brain, as well as enhance the efficiency of the hybrid KNN algorithm and Laplace transformation make it deal the non-zero values in terms of missing values form with the using of Frobenius Matrix for deal the space into non-zero values. Our proposed algorithm takes the longest values of KNN (K = 1-100), which is successfully demonstrated in a 4-dimensional modulation method that monitors the lighting field that can be used in the field of light emission. Conclusion: This approach dramatically improves the efficiency of hybrid KNN method and the detection of tumor region using 4-D segmentation method. The simulation results verified the performance of the proposed method is improved by 92% sensitivity of 60% specificity and 70.50% accuracy respectively.