• 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.

• International Journal of Control, Automation, and Systems
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• v.2 no.1
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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.

• Journal of Biomedical Engineering Research
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• v.40 no.3
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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.

• 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.

• Journal of the Korean Society of Radiology
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• v.18 no.6
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• pp.691-698
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• 2024

Brain tumors arise from various complex factors, including genetic, environmental, immunological, and biochemical influences. They can be classified as primary or metastatic, differing in their origin and location. Brain tumors significantly impact the quality of life, leading to symptoms such as headaches, seizures, cognitive decline, and motor function impairment, depending on the tumor's size and location. Early diagnosis of brain tumors is crucial for improving quality of life. Timely detection allows for prompt treatment initiation, which can prevent tumor growth and the worsening of symptoms. Diagnosis typically involves neurological examinations, imaging examinations, tissue biopsies, and blood tests. In particular, MRI provides high-resolution images of the brain's detailed structure, clearly depicting the location, size, shape, and surrounding tissues of the tumor. This study proposes a method for detecting and segmenting brain tumors in MRI images, utilizing a dataset constructed for this purpose, named "BrainTumors_1.0.zip." Experimental results demonstrate that filtering the input images enhances image quality and enables accurate tumor detection. Future research will focus on enhancing algorithm generalization, diversifying the dataset, developing automated methodologies, and assessing clinical utility to establish an effective tool for the diagnosis and treatment of brain tumors.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.10
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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.

• Journal of the Institute of Convergence Signal Processing
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• v.12 no.4
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• pp.267-273
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• 2011

In this paper, we proposed the method to detect brain tumor region in MR images. Our method is composed of 3 parts, detection of tumor slice, detection of tumor region and tumor boundary detection. In the tumor slice detection step, a slice which contains tumor regions is distinguished using symmetric analysis in 3D brain volume. The tumor region detection step is the process to segment the tumor region in the slice distinguished as a tumor slice. And tumor region is finally detected, using spatial feature and symmetric analysis based on the cluster information. The process for detecting tumor slice and tumor region have advantages which are robust for noise and requires less computational time, using the knowledge of the brain tumor and cluster-based on symmetric analysis. And we use the level set method with fast marching algorithm to detect the tumor boundary. It is performed to find the tumor boundary for all other slices using the initial seeds derived from the previous or later slice until the tumor region is vanished. It requires less computational time because every procedure is not performed for all slices.

• 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.

• Brain Tumor Research and Treatment
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• v.6 no.2
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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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• v.21 no.2
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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.