• International Journal of Advanced Culture Technology
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• v.6 no.4
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• pp.262-265
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• 2018

Deep learning, a sub-field of machine learning changing the prospects of artificial intelligence (AI) because of its recent advancements and application in various field. Deep learning deals with algorithms inspired by the structure and function of the brain called artificial neural networks. This works reviews basic architecture and recent advancement of deep structured learning. It also describes contemporary applications of deep structured learning and its advantages over the treditional learning in artificial interlligence. This study is useful for the general readers and students who are in the early stage of deep learning studies.

• The Korean Journal of Nuclear Medicine Technology
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• v.19 no.1
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• pp.12-16
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• 2015

Purpose Principal component analysis (PCA) is a method often used in the neuroimagre analysis as a multivariate analysis technique for describing the structure of high dimensional correlation as the structure of lower dimensional space. PCA is a statistical procedure that uses an orthogonal transformation to convert a set of observations of correlated variables into a set of values of linearly independent variables called principal components. In this study, in order to investigate the usefulness of PCA in the brain PET image analysis, we tried to analyze C[11]-PIB PET image as a representative case. Materials and Methods Nineteen subjects were included in this study (normal = 9, AD/MCI = 10). For C[11]-PIB, PET scan were acquired for 20 min starting 40 min after intravenous injection of 9.6 MBq/kg C[11]-PIB. All emission recordings were acquired with the Biograph 6 Hi-Rez (Siemens-CTI, Knoxville, TN) in three-dimensional acquisition mode. Transmission map for attenuation-correction was acquired using the CT emission scans (130 kVp, 240 mA). Standardized uptake values (SUVs) of C[11]-PIB calculated from PET/CT. In normal subjects, 3T MRI T1-weighted images were obtained to create a C[11]-PIB template. Spatial normalization and smoothing were conducted as a pre-processing for PCA using SPM8 and PCA was conducted using Matlab2012b. Results Through the PCA, we obtained linearly uncorrelated independent principal component images. Principal component images obtained through the PCA can simplify the variation of whole C[11]-PIB images into several principal components including the variation of neocortex and white matter and the variation of deep brain structure such as pons. Conclusion PCA is useful to analyze and extract the main pattern of C[11]-PIB image. PCA, as a method of multivariate analysis, might be useful for pattern recognition of neuroimages such as FDG-PET or fMRI as well as C[11]-PIB image.

• Journal of the Korean Society of Radiology
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• v.14 no.5
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• pp.585-595
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• 2020

Magnetic resonance imaging(MRI) has become an important technique for examining changes in human brain structure with neurological disorders. Brain development is a very complex process, and is affected by neurogenesiss and genetic programs. As age increases, structures of the brain change, which can contribute to the formation of brain diseases. Among the various factors, Gender is one of the greatest influential factors that affect the development of a healthy brain. The images were analyzed through various programs found in FSL such as SIENAX, FIRST, and Vertex analysis. Our results show that significant gender-related differences in subcortical areas were observed at the particular age group. The magnitude of these differences between gender and volume varied depending on the area investigated. In this study, we used more advanced 3T MRI for the structural analysis of subcortical structures between each gender. In addition, Vertex Analysis was used to visualize the volumetric differences in subcortical structures between each gender. This study is limited to groups in their 70s, therefore, further studies are needed for wider age groups.

• The KIPS Transactions:PartA
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• v.13A no.5 s.102
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• pp.465-472
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• 2006

Recently, many suggestions have been made in image segmentation methods for extracting human organs or disease affected area from huge amounts of medical image datasets. However, images from some areas, such as brain, which have multiple structures with ambiruous structural borders, have limitations in their structural segmentation. To address this problem, clustering technique which classifies voxels into finite number of clusters is often employed. This, however, has its drawback, the influence from noise, which is caused from voxel by voxel operations. Therefore, applying image enhancing method to minimize the influence from noise and to make clearer image borders would allow more robust structural segmentation. This research proposes an efficient structural segmentation method by filtering based clustering to extract detail structures such as white matter, gray matter and cerebrospinal fluid from brain MR. First, coherence enhancing diffusion filtering is adopted to make clearer borders between structures and to reduce the noises in them. To the enhanced images from this process, fuzzy c-means clustering method was applied, conducting structural segmentation by assigning corresponding cluster index to the structure containing each voxel. The suggested structural segmentation method, in comparison with existing ones with clustering using Gaussian or general anisotropic diffusion filtering, showed enhanced accuracy which was determined by how much it agreed with the manual segmentation results. Moreover, by suggesting fine segmentation method on the border area with reproducible results and minimized manual task, it provides efficient diagnostic support for morphological abnormalities in brain.

• Korean Journal of Veterinary Research
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• v.40 no.1
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• pp.1-16
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• 2000

Nowadays, mongolian gerbil is notably utilized for the research of brain and water deprivation because of a congenital incomplete willis circle structure in the brain, audiogenic seizure in low noise, and special cholesterol metabolism without water absorption for a long time. In this study, we are intend to identify the morphological changes of the catecholaminergic neuron of brain according to the time lapse in the condition of long term water deprivation. 55 mongolian gerbil were divided 10 groups(control, 1, 2, 3, 4, 5, 10, 15, 20, 42th day water deprivation group), of which each group include 5 mongolian gerbils and 5 normal mongolian gerbils in control group were also used for brain atlas as a control. The brains were observed by the immunohistochemical stain using the TH, DBH and PMNT antibody. The results were as followings; 1. The nerve fibers of the TH-immunoreactive neuron were observed only in the and corpus striatum of the telencephalon. 2. Intensity of the immunostain of the nerve fiber in the cerebral cortex and corpus striatum was decreased gradually day by day after water deprivation. 3. The TH-immunoreactive nerve cells were observed in the paraventricular and periventricular nucleus of the 3rd ventricular in the hypothalamus of mongolian gerbil but the number of nerve cells were decreased from the first day of the water deprivation to the 10th day and increased until the 20th day, after than redecreased from the 20th day by the continuous water deprivation. The number of nerve fibers in this area were increased in the first day, but decreased from the 2nd day of water deprivation. The shape and density of the dopamine secreting cells in the brain of mongolian gerbil by the immunoreactive stain were changed in the continuous water deprivation. In this results, we can conclude that dopamine concerned in the water metabolism of mongolian gerbil, and mongolian gerbil could be used as an animal model for the research of water deprivation.

• Maxillofacial Plastic and Reconstructive Surgery
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• v.31 no.2
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• pp.103-115
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• 2009

In vertebrates, the face is mainly formed with neural crest derived neural crest cells by the inherent programs and the interactive environmental factors. Extracellular signaling-regulated kinase (Erk) is one of such programs to regulate the various cellular functions. And retinoic acid (RA) also plays an important role as a regulator in differentiation process at various stages of vertebrate embryogenesis. We wanted to know that the segregation as well as the patterning of maxillary and mandibular structure is greatly influenced by the maxillomandibular cleft (MMC) and the failure of this development may result in the maxillomandibular fusion (syngnathia) or other patterning related disorder. It has been well documented that the epithelium at this cleft region has significant expression of Fibroblast growth factor (Fgf) 8, and it is essential for the patterning of the first arch derived structures. By the morphological, skeletal, cell proliferation and apoptotic, and hybridization analysis, we checked the effects of Erk inhibition and/or RA activation onto MMC and could observe that Erk and RA signaling is individually and synergically involved in the facial patterning in terms of FGF signaling pathway via Barx-l. So RA and Erk signaling work together for the MMC patterning and the segregation of maxilla-mandible by controlling the Fgf-related signaling pathways. And the abnormality in MMC brought by aberrant Fgf signaling may result in the disturbances of maxillary-mandibular segregation.

• Journal of the Korea Society for Simulation
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• v.18 no.4
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• pp.197-206
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• 2009

The medical information system is an effective medical diagnosis assistance system which offers an environment in which medial images and diagnosis information can be shared. However, this system can only stored and transmitted information without other functions. To resolve this problem and to enhance the efficiency of diagnostic activities, a medical image classification and retrieval system is necessary. The medical image classification and retrieval system can improve efficiency in a medical diagnosis by providing disease-related images and can be useful in various medical practices by checking diverse cases. However, it is difficult to understand the meanings contained in images because the existing image classification and retrieval system has handled superficial information only. Therefore, a medical image classification system which can classify medical images by analyzing the relation among the elements of the image as well as the superficial information has been required. In this paper, we propose the method for learning and classification of brain MRI, in which the superficial information as well as the spatial information extracted from images are used. The superficial information of images, which is color, shape, etc., is called low-level image information and the logical information of the image is called high-level image information. In extracting both low-level and high-level image information in this paper, the anatomical names and structure of the brain have been used. The low-level information is used to give an anatomical name in brain images and the high-level image information is extracted by analyzing the relation among the anatomical parts. Each information is used in learning and classification. In an experiment, the MRI of the brain including disease have been used.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.101-106
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• 2023

In this busy world actually stress is continuously grow up in research and monitoring social websites. The social interaction is a process by which people act and react in relation with each other like play, fight, dance we can find social interactions. In this we find social structure means maintain the relationships among peoples and group of peoples. Its a limit and depends on its behavior. Because relationships established on expectations of every one involve depending on social network. There is lot of difference between emotional pain and physical pain. When you feel stress on physical body we all feel with tensions, stress on physical consequences, physical effects on our health. When we work on social network websites, developments or any research related information retrieving etc. our brain is going into stress. Actually by social network interactions like watching movies, online shopping, online marketing, online business here we observe sentiment analysis of movie reviews and feedback of customers either positive/negative. In movies there we can observe peoples reaction with each other it depends on actions in film like fights, dances, dialogues, content. Here we can analysis of stress on brain different actions of movie reviews. All these movie review analysis and stress on brain can calculated by machine learning techniques. Actually in target oriented business, the persons who are working in marketing always their brain in stress condition their emotional conditions are different at different times. In this paper how does brain deal with stress management. In software industries when developers are work at home, connected with clients in online work they gone under stress. And their emotional levels and stress levels always changes regarding work communication. In this paper we represent emotional intelligence with stress based analysis using machine learning techniques in social networks. It is ability of the person to be aware on your own emotions or feeling as well as feelings or emotions of the others use this awareness to manage self and your relationships. social interactions is not only about you its about every one can interacting and their expectations too. It about maintaining performance. Performance is sociological understanding how people can interact and a key to know analysis of social interactions. It is always to maintain successful interactions and inline expectations. That is to satisfy the audience. So people careful to control all of these and maintain impression management.

• Korean Journal of Computational Design and Engineering
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• v.5 no.4
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• pp.359-372
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• 2000

Current CAD systems are good enough to be used as a tool to manipulate three-dimensional shapes. This is a very important capability to be owned by a design tool because a major portion of designers'activities is spent on the shape manipulation in the design detailing process. However, the whole design process involves a lot more than the, shape manipulation. Currently, these remaining tasks, mostly logical reasoning process for the function realization together with structure decomposition in the top-down manner, are processed in the designer's brain. To support the top-down functional design process of a mechanical product, a system integrating the functional, structural and geometrical aspects of a product design in a unified environment is presented. Using this system, a designer can perform function decomposition, structure decomposition, and geometry detailing, and function verification activities in parallel and the whole design process it modeled resultantly. Once the whole design process is modeled, any redesign task can be automatically performed with the verification of the desired functions.

• Speech Sciences
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• v.4 no.2
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• pp.103-113
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• 1998

The human baby appears to be born with preexistent knowledge of language. This specialized neural structure in the brain awaits auditory experience with language to trigger it into functioning. The auditory-linked acquisition of language is a time-locked function related to early maturational periods in the infant's life. The longer auditory language stimulation is delayed, the less efficient the language facility will be. It is for these reasons that it is urgent to fight the hearing problems of children with all the skill, knowledge and insights of which we are capable, the so called 'rehabilitative process'. An understanding of the timetable and the origin of prenatal to early life development of auditory mechanism will help in planning the aural rehabilitation. Further interests and studies are needed to establish the systematic structure of rehabilitative audiology.