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            <name>Title</name>
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                <text>Articles</text>
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    <name>Article</name>
    <description>Faculty Publications -Articles</description>
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        <element elementId="50">
          <name>Title</name>
          <description>A name given to the resource</description>
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              <text>Segmentation and identification of MRI Brain segment in digital image</text>
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          <name>Subject</name>
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              <text>Classification; Image processing; MRI; Pattern recognition; Segmentation</text>
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              <text>Brain image segmentation is important in the area of clinical diagnosis. MRI Brain image segmentation is time consuming and there is always a chance of occurrence of error when the segmentation is done manually. It is always possible to detect the infected tissues easily in the current medical field. However, the accuracy and the characteristics of abnormalities of the tissues are not precise. In the past, many researchers have identified the drawbacks of manual segmentation and hence proposed the semiautomatic and fully automatic segmentation methods in the field of medical imaging. The amount of precision about the detection of defective tissues leads to acceptance of a particular image segmentation method. In this article three segmentation methods are hybridized to get the optimum extraction of the region of interest (ROI) in brain MRI image. Further, the region properties of segment is extracted and stored as knowledgebase. The proposed algorithm integrates multiple segmentation methods and identifies the Brain Outer layer in MRI image. This identification AIDS medical experts for optimum diagnosis of defective tissues in the brain.  IAEME Publication.</text>
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          <name>Creator</name>
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              <text>Basil T.; Hiremath M.</text>
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          <name>Source</name>
          <description>A related resource from which the described resource is derived</description>
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              <text>International Journal of Mechanical Engineering and Technology, Vol-9, No. 3, pp. 174-183.</text>
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          <name>Publisher</name>
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              <text>IAEME Publication</text>
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          <name>Date</name>
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              <text>2018-01-01</text>
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          <name>Identifier</name>
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              <text>&lt;a href="" target="_blank" rel="noreferrer noopener"&gt;&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85044763274&amp;amp;partnerID=40&amp;amp;md5=12527d44e5bd932708df493602b83fb6" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85044763274&amp;amp;partnerID=40&amp;amp;md5=12527d44e5bd932708df493602b83fb6&lt;/a&gt;</text>
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          <name>Rights</name>
          <description>Information about rights held in and over the resource</description>
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              <text>Restricted Access</text>
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          <name>Relation</name>
          <description>A related resource</description>
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              <text>ISSN: 9766340</text>
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          <name>Format</name>
          <description>The file format, physical medium, or dimensions of the resource</description>
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              <text>Online</text>
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          <name>Language</name>
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            <elementText elementTextId="133903">
              <text>English</text>
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          <name>Type</name>
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              <text>Article</text>
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              <text>Basil T., Department of Computer Science, Christ University, Hosur Road, Bengaluru, India; Hiremath M., Department of Computer Science, Christ University, Hosur Road, Bengaluru, India</text>
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