• 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.3 no.3
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• pp.47-52
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• 2016

This research paper examines the usage of graphic imaging in Holographic Projections to further advance the medical field. It highlights the importance and necessity of this technology as well as avant-garde techniques applied in the process of displaying images in digital holography. This paper also discusses the different types of applications for holograms in society today. Different tools were utilized to transfer a set of a cancer patient's brain tumor data into data used to produce a 3D holographic image. This image was produced through the transfer of data from one program to another. Through the use of semi-automatic segmentation through the seed region method, we were able to create a 3D visualization from Computed Tomography (CT) data.

• 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.

• Investigative Magnetic Resonance Imaging
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• v.21 no.1
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• pp.9-19
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• 2017

Background: Normalized cerebral blood volume (nCBV) can be measured using manual or semiautomatic segmentation method. However, the difference in diagnostic performance on brain tumor differentiation between differently measured nCBV has not been evaluated. Purpose: To compare the diagnostic performance of manually obtained nCBV to that of semiautomatically obtained nCBV on glioblastoma (GBM) and primary central nervous system lymphoma (PCNSL) differentiation. Materials and Methods: Histopathologically confirmed forty GBM and eleven PCNSL patients underwent 3T MR imaging with dynamic susceptibility contrast-enhanced perfusion MR imaging before any treatment or biopsy. Based on the contrast-enhanced T1-weighted imaging, the mean nCBV (mCBV) was measured using the manual method (manual mCBV), random regions of interest (ROIs) placement by the observer, or the semiautomatic segmentation method (semiautomatic mCBV). The volume of enhancing portion of the tumor was also measured during semiautomatic segmentation process. T-test, ROC curve analysis, Fisher's exact test and multivariate regression analysis were performed to compare the value and evaluate the diagnostic performance of each parameter. Results: GBM showed a higher enhancing volume (P = 0.0307), a higher manual mCBV (P = 0.018) and a higher semiautomatic mCBV (P = 0.0111) than that of the PCNSL. Semiautomatic mCBV had the highest value (0.815) for the area under the curve (AUC), however, the AUCs of the three parameters were not significantly different from each other. The semiautomatic mCBV was the best independent predictor for the GBM and PCNSL differential diagnosis according to the stepwise multiple regression analysis. Conclusion: We found that the semiautomatic mCBV could be a better predictor than the manual mCBV for the GBM and PCNSL differentiation. We believe that the semiautomatic segmentation method can contribute to the advancement of perfusion based brain tumor evaluation.

• Journal of Korea Multimedia Society
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• v.9 no.9
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• pp.1150-1159
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• 2006

This paper proposed a curve progress control function of the area base instead of the existing edge indication function, in order to detect the brain ventricle area by utilizing a geodesic active contour model. The proposed curve progress control function is very effective in detecting the brain ventricle area and this function is based on the average brightness of the brain ventricle area which appears brighter in MRI images. Compared numerically by using various measures, the proposed method in this paper can detect brain ventricle areas better than the existing method. By examining images of normal and diseased brain's images by brain tumor, we compared the several brain ventricle detection algorithms with proposed method visually and verified the effectiveness of the proposed method.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.05a
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• pp.474-476
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• 2021

Glioblastoma is the most common brain malignancies arising from glial cells. Early diagnosis and treatment plan establishment are important, and cancer is diagnosed mainly through T1CE imaging through injection of a contrast agent. However, the risk of injection of gadolinium-based contrast agents is increasing recently. Region segmentation that marks cancer regions in medical images plays a key role in CAD systems, and deep neural network models for synthesizing new images are also being studied. In this study, we propose a model that simultaneously learns the generation of T1CE images and segmentation of cancer regions. The performance of the proposed model is evaluated using similarity measurements including mean square error and peak signal-to-noise ratio, and shows average result values of 21 and 39 dB.

• Investigative Magnetic Resonance Imaging
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• v.19 no.2
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• pp.88-98
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• 2015

Purpose: To compare the interobserver and intraobserver reliability of mean apparent diffusion coefficient (ADC) values using contrast-enhanced (CE) T1 weighted image (WI) and T2WI as structural images between manual and semiautomatic segmentation methods. Materials and Methods: Between January 2011 and May 2013, 28 patients who underwent brain MR with diffusion weighted image (DWI) and were pathologically confirmed as having glioblastoma participated in our study. The ADC values were measured twice in manual and semiautomatic segmentation methods using CE-T1WI and T2WI as structural images to obtain interobserver and intraobserver reliability. Moreover, intraobserver reliabilities of the different segmentation methods were assessed after subgrouping of the patients based on the MR findings. Results: Interobserver and intraobserver reliabilities were high in both manual and semiautomatic segmentation methods on CE-T1WI-based evaluation, while interobserver reliability on T2WI-based evaluation was not high enough to be used in a clinical context. The intraobserver reliability was particularly lower with the T2WI-based semiautomatic segmentation method in the subgroups with involved $lobes{\leq}2$, with partially demarcated tumor borders, poorly demarcated inner margins of the necrotic portion, and with perilesional edema. Conclusion: Both the manual and semiautomatic segmentation methods on CE-T1WI-based evaluation were clinically acceptable in the measurement of mean ADC values with high interobserver and intraobserver reliabilities.

• Korean Chemical Engineering Research
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• v.57 no.2
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• pp.274-282
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• 2019

In recent years, big data analysis has been expanded to include automatic control through reinforcement learning as well as prediction through modeling. Research on the utilization of image data is actively carried out in various industrial fields such as chemical, manufacturing, agriculture, and bio-industry. In this paper, we applied NASNet, which is an AutoML reinforced learning algorithm, to DeepU-Net neural network that modified U-Net to improve image semantic segmentation performance. We used BRATS2015 MRI data for performance verification. Simulation results show that DeepU-Net has more performance than the U-Net neural network. In order to improve the image segmentation performance, remove dropouts that are typically applied to neural networks, when the number of kernels and filters obtained through reinforcement learning in DeepU-Net was selected as a hyperparameter of neural network. The results show that the training accuracy is 0.5% and the verification accuracy is 0.3% better than DeepU-Net. The results of this study can be applied to various fields such as MRI brain imaging diagnosis, thermal imaging camera abnormality diagnosis, Nondestructive inspection diagnosis, chemical leakage monitoring, and monitoring forest fire through CCTV.