• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.3
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• pp.1167-1187
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• 2020

With the development of medical imaging technology, image registration has been widely used in the field of disease diagnosis. The registration between different modal images of brain magnetic resonance (MR) is particularly important for the diagnosis of brain diseases. However, previous registration methods don't take advantage of the prior knowledge of bilateral brain symmetry. Moreover, the difference in gray scale information of different modal images increases the difficulty of registration. In this paper, a multimodal medical image registration method based on image segmentation and symmetric self-similarity is proposed. This method uses modal independent self-similar information and modal consistency information to register images. More particularly, we propose two novel symmetric self-similarity constraint operators to constrain the segmented medical images and convert each modal medical image into a unified modal for multimodal image registration. The experimental results show that the proposed method can effectively reduce the error rate of brain MR multimodal medical image registration with rotation and translation transformations (average 0.43mm and 0.60mm) respectively, whose accuracy is better compared to state-of-the-art image registration methods.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.12 no.5
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• pp.39-54
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• 2012

Picture Archiving and Communication System (PACS) has been planted as one of the key infrastructures with an overall improvement in standards of medical informationization and the stream of digital hospitalization in recent days. The kind and data of digital medical imagery are also increasing rapidly in volume. This trend emphasizes the medical image compression for storing large-scale medical image data. Digital Imaging and Communications in Medicine (DICOM), de facto standard in digital medical imagery, specifies Run Length Encode (RLE), which is the typical lossless data compressing technique, for the medical image compression. However, the RLE is not appropriate approach for medical image data with bilateral symmetry of the human organism. we suggest two preprocessing algorithms that detect interested area, the minimum bounding rectangle, in a medical image to enhance data compression efficiency and that re-code image pixel values to reduce data size according to the symmetry characteristics in the interested area, and also presents an improved image compression technique for brain CT imagery with high bilateral symmetry. As the result of experiment, the suggested approach shows higher data compression ratio than the RLE compression in the DICOM standard without detecting interested area in images.

• Journal of Korea Game Society
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• v.15 no.2
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• pp.123-130
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• 2015

In this paper, we present the research result related to a game application system which improves the symmetry of the left and right brain activity for alleviating the emotional disorders such as anxiety and depression. Since the characteristics of patients with affective disorders are less willing to therapy. obvious motivation is needed in general. To provide a strong incentive for these patients, we propose a customized game system through game-oriented content of enjoyment. After the experiments conducted for 5 days, it was found that the symmetry of left and right brain activity is enhanced. The proposed functional game system can be applied to a wide range of applications such as healthcare or education.

• Proceedings of the KSMRM Conference
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• 2002.11a
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• pp.133-133
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• 2002

Purpose： The purpose of paper is to implement software to visualize brain fiber tract using a 24-bit color coding scheme and to test its feasibility. Materials and Methods： MR imaging was performed on GE 1.5 T Signa scanner. For diffusion tensor image, we used a single shot spin-echo EPI sequence with 7 non-colinear pulsed-field gradient directions： (x, y, z)：(1,1,0),(-1,1,0),(1,0,1),(-1,0,1),(0,1,1),(0,1,-1) and without diffusion gradient. B-factor was 500 sec/$\textrm{mm}^2$. Acquisition parameters are as follows： TUTE=10000ms/99ms, FOV=240mm, matrix=128${\times}$128, slice thickness/gap=6mm/0mm, total slice number=30. Subjects consisted of 10 normal young volunteers (age：21∼26 yrs, 5 men, 5 women). All DTI images were smoothed with Gaussian kernel with the FWHM of 2 pixels. Color coding schemes for visualization of directional information was as follows. HSV(Hue, Saturation, Value) color system is appropriate for assigning RGB(Red, Green, and Blue) value for every different directions because of its volumetric directional expression. Each of HSV are assigned due to (r,$\theta$,${\Phi}$) in spherical coordinate. HSV calculated by this way can be transformed into RGB color system by general HSV to RGB conversion formula. Symmetry schemes： It is natural to code the antipodal direction to be same color(antipodal symmetry). So even with no symmetry scheme, the antipodal symmetry must be included. With no symmetry scheme, we can assign every different colors for every different orientation.(H =${\Phi}$, S=2$\theta$/$\pi$, V=λw, where λw is anisotropy). But that may assign very discontinuous color even between adjacent yokels. On the other hand, Full symmetry or absolute value scheme includes symmetry for 180$^{\circ}$ rotation about xy-plane of color coordinate (rotational symmetry) and for both hemisphere (mirror symmetry). In absolute value scheme, each of RGB value can be expressed as follows. R=λw｜Vx｜, G=λw｜Vy｜, B=λw｜Vz｜, where (Vx, Vy, Vz) is eigenvector corresponding to the largest eigenvalue of diffusion tensor. With applying full symmetry or absolute value scheme, we can get more continuous color coding at the expense of coding same color for symmetric direction. For better visualization of fiber tract directions, Gamma and brightness correction had done. All of these implementations were done on the IDL 5.4 platform.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.1
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• pp.105-125
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• 2024

Gliomas are the most common malignant brain tumor and cause the most deaths. Manual brain tumor segmentation is expensive, time-consuming, error-prone, and dependent on the radiologist's expertise and experience. Manual brain tumor segmentation outcomes by different radiologists for the same patient may differ. Thus, more robust, and dependable methods are needed. Medical imaging researchers produced numerous semi-automatic and fully automatic brain tumor segmentation algorithms using ML pipelines and accurate (handcrafted feature-based, etc.) or data-driven strategies. Current methods use CNN or handmade features such symmetry analysis, alignment-based features analysis, or textural qualities. CNN approaches provide unsupervised features, while manual features model domain knowledge. Cascaded algorithms may outperform feature-based or data-driven like CNN methods. A revolutionary cascaded strategy is presented that intelligently supplies CNN with past information from handmade feature-based ML algorithms. Each patient receives manual ground truth and four MRI modalities (T1, T1c, T2, and FLAIR). Handcrafted characteristics and deep learning are used to segment brain tumors in a Global Convolutional Neural Network (GCNN). The proposed GCNN architecture with two parallel CNNs, CSPathways CNN (CSPCNN) and MRI Pathways CNN (MRIPCNN), segmented BraTS brain tumors with high accuracy. The proposed model achieved a Dice score of 87% higher than the state of the art. This research could improve brain tumor segmentation, helping clinicians diagnose and treat patients.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.4
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• pp.430-440
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• 2021

The purpose of this study was to analyze the degree of depression, cognitive function, communication ability, and the quantitative electroencephalogram (EEG) in elderly individuals with hearing loss and to investigate their inter-relationship. Hearing-impaired elderly participants, aged 60 years or older (37 men and 26 women) who visited the S Hearing Rehabilitation Center in Y City from June 20, 2020, to September 3, 2020, participated voluntarily after a recruitment announcement.The participants' overall characteristics, depression, and cognitive functions were evaluated with a structured questionnaire. The Word Recognition Score (WRS) was evaluated with an audiometer using the Korean Standard Monosyllabic Word Lists for Adults (KS-MWL-A). The quantitative EEG was measured with dry electrodes using a 2-channel EEG on the frontal lobes Fp1 and Fp2. The results are summarized as follows: Communication ability showed a positive correlation with the left-right symmetry of the frontal lobes (**p<.01) and a negative correlation with right-brain mental distraction and stress (*p<.05). In the difference WRS test for each group, the left-right symmetry of the frontal lobes (**p<.01) showed the greatest correlation with communication ability. Our results suggest that the left-right symmetry of the frontal lobes can be a biomarker indicative of the communication ability of older people with hearing impairments.

• Journal of Biomedical Engineering Research
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• v.20 no.2
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• pp.149-154
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• 1999

In anatomical aspects, magnetic resonance image offers more accurate information than other medical images such as X ray, ultrasonic and CT images. This paper introduces a method that segments and detects lesion for 2 dimensional axial MR brain images automatically. Image segmentation process consists of 2 stages. First stage extracts cerebrum region using thresholding and morphology. In the second stage, white matter, gray matter and cerebrospinal fluid in the cerebrum are extracted using FCM, We could improve processing time as removing uninterested region. Finally symmetry measure and anatomical Knowledge are used to detect lesion.

• The Journal of Korean Physical Therapy
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• v.14 no.1
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• pp.125-130
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• 2002

This study was applied using vojta therapy in the patient with hydrocephalus occurred by on traumatic brain injury. Vojta treatment was a recently developed of the brain damage patient treatment which can be applied eariler than the other traditional methods. The results were as follows. 1. Hip joint flexion contracture from $100^{\circ}$ to $15^{\circ}$ was improved on prone position. 2. Left convexity curve on thracolumbar region due to functional scoliosis the normal aligment. 3. The thumb finger was changed from thumb-in to thumb-out. 4. Right tilted pelvis on prone position became the normal symmetry

• Journal of the Korean Society of Physical Medicine
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• v.8 no.3
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• pp.351-360
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• 2013

PURPOSE: The purpose of this study was to investigate the effects of observed action gait training on stroke patients. METHODS: 22 subjects were randomized into two groups. The observed action gait training performed that watched a video of normal gait before gait training and the general gait training without watching it. The experimental group(n=11) performed observed action gait training and the control group(n=11) performed general gait training. Both group received gait training for 3 times per week during 8 weeks. RESULTS: The experimental group showed significant differences in the cadence, gait velocity, stride, step, single limb support, double limb support, stride length and step length(p<.05). The control group showed significant differences only in the stride(p<.05). CONCLUSION: The observed action gait training affected coordination and weight shift, as well as symmetry of the body. Plasticity of the brain was facilitated by repetitive visual and sensory stimulation. The observed action gait training promoted the normal gait by watching the normal gait pattern. In conclusion, motor learning through the sensory stimulation promotes brain plasticity that could improve motor function, and observed action gait training indirectly identified stimulated brain activities.