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<!-- views/paperById.ejs --> <!DOCTYPE html> <html> <head> <meta charset="utf-8"> <title>SCITEPRESS - SCIENCE AND TECHNOLOGY PUBLICATIONS</title> <meta name ="description" content="Digital Library" /> <meta name="citation_language" content="en"> <meta name="citation_title" content="Detecting Tourette鈥檚 Syndrome in Anatomical Regions of the Brain through MRI Analysis and Naive Bayes Classifier"> <meta name="citation_abstract" content="Tourette Syndrome (TS) is an inherited condition represented by involuntary vocal and motor movements (tics). Nowadays, there is no available cure, only psychological treatments to inhibit it, requesting the use of medication in rare cases. The importance of diagnosing Tourette鈥檚 in childhood enables a range of possible treatments that would decrease the intensity of TS, and in some cases, even stop it. In most cases, the TS diagnosis considers only clinical assessment. Analyzing the brain and its anatomical regions via imaging data can provide relevant information in order to assist doctors. This work aims to propose an approach in order to identify the most affected anatomical region of the brain by TS. The approach consists of three major steps: (i) the brain is segmented in its anatomical regions; (ii) texture patterns are extracted via Gray-level Co-occurrence Matrix for each region; finally, (iii) each brain region is evaluated using Naive Bayes classifier, determining the presence or absence of TS. We use MRI images from 68 subjects around nine years old equally divided whether has TS or not. The regions from the limbic system were relevant in the diagnosis: right-side accumbens reached 68% of accuracy; posterior and central parts of corpus callosum ranked in the top four positions. Combining the top five most predictive regions led our approach to reach 78% of accuracy. The results were significant in detecting the most affected regions in TS and providing a reliable approach to classify the brain regions accordingly."> <meta name="citation_publication_date" content="2022/04/22"> <meta name="citation_conference_title" content="International Conference on Image Processing and Vision Engineering"> <meta name="citation_keywords" content="Classification; Tourette Syndrome; GLCM; Naive Bayes; Image Processing; Segmentation;"> <meta name="citation_doi" content="10.5220/0011056800003209"> <meta name="citation_isbn" content="978-989-758-563-0"> <meta name="citation_volume" content="2"> <meta name="citation_firstpage" content="26"> <meta name="citation_lastpage" content="33"> <meta name="citation_publisher" content="SCITEPRESS"> <meta name="citation_author" content="Murilo Costa De Barros" > <meta name="citation_author_institution" content="Computing Visual Laboratory, School of Technology UNICAMP, R. Paschoal Marmo, 1888, Jd. Nova It谩lia, 13484-332, Limeira, S茫o Paulo, Brazil" > <meta name="citation_author" content="Kaue Duarte" > <meta name="citation_author_institution" content="Vascular Imaging Laboratory, Calgary University, 2500 University Dr. NW, Calgary, AB T2N 1N4, Canada" > <meta name="citation_author" content="Wang-Tso Lee" > <meta name="citation_author_institution" content="Department of Pediatrics, National Taiwan University Children鈥檚 Hospital, Taipei, Taiwan" > <meta name="citation_author" content="Chia-Jui Hsu" > <meta name="citation_author_institution" content="Department of Pediatrics, National Taiwan University Hospital Hsinchu Branch, Taipei, Taiwan" > <meta name="citation_author" content="Marco Garcia De Carvalho" > <meta name="citation_author_institution" content="Computing Visual Laboratory, School of Technology UNICAMP, R. Paschoal Marmo, 1888, Jd. Nova It谩lia, 13484-332, Limeira, S茫o Paulo, Brazil" > <meta name="citation_abstract_html_url" content="/PublishedPapers/2022/110568"> <meta name="citation_pdf_url" content="/PublishedPapers/2022/110568/110568.pdf"> </head> <body> <article> <a href="/PublishedPapers/2022/110568/pdf/index.html"><h1 class="citation_title">Detecting Tourette鈥檚 Syndrome in Anatomical Regions of the Brain through MRI Analysis and Naive Bayes Classifier</h1></a> <h3 class="citation_author"> Murilo Costa De Barros, Kaue Duarte, Wang-Tso Lee, Chia-Jui Hsu, Marco Garcia De Carvalho</h3> <h4 class="citation_publication_date">2022</h4> <h4>Abstract</h4> <p class="citation_abstract">Tourette Syndrome (TS) is an inherited condition represented by involuntary vocal and motor movements (tics). Nowadays, there is no available cure, only psychological treatments to inhibit it, requesting the use of medication in rare cases. The importance of diagnosing Tourette鈥檚 in childhood enables a range of possible treatments that would decrease the intensity of TS, and in some cases, even stop it. In most cases, the TS diagnosis considers only clinical assessment. Analyzing the brain and its anatomical regions via imaging data can provide relevant information in order to assist doctors. This work aims to propose an approach in order to identify the most affected anatomical region of the brain by TS. The approach consists of three major steps: (i) the brain is segmented in its anatomical regions; (ii) texture patterns are extracted via Gray-level Co-occurrence Matrix for each region; finally, (iii) each brain region is evaluated using Naive Bayes classifier, determining the presence or absence of TS. We use MRI images from 68 subjects around nine years old equally divided whether has TS or not. The regions from the limbic system were relevant in the diagnosis: right-side accumbens reached 68% of accuracy; posterior and central parts of corpus callosum ranked in the top four positions. Combining the top five most predictive regions led our approach to reach 78% of accuracy. The results were significant in detecting the most affected regions in TS and providing a reliable approach to classify the brain regions accordingly.</p> <a href="/PublishedPapers/2022/110568/110568.pdf" class="citation_pdf_url">Download</a> <br /> <br /> <br/> <h4 style="margin:0;">Paper Citation</h4> <br/> <h4 style="margin:0;">in Harvard Style</h4> <p style="margin:0;">Costa De Barros M., Duarte K., Lee W., Hsu C. and Garcia De Carvalho M. (2022). <b>Detecting Tourette鈥檚 Syndrome in Anatomical Regions of the Brain through MRI Analysis and Naive Bayes Classifier</b>. In <i>Proceedings of the 2nd International Conference on Image Processing and Vision Engineering - Volume 1: IMPROVE,</i> ISBN 978-989-758-563-0, pages 26-33. DOI: 10.5220/0011056800003209</p> <br/> <h4 style="margin:0;">in Bibtex Style</h4> <p style="margin:0;">@conference{improve22,<br />author={Murilo Costa De Barros and Kaue Duarte and Wang-Tso Lee and Chia-Jui Hsu and Marco Garcia De Carvalho},<br />title={Detecting Tourette鈥檚 Syndrome in Anatomical Regions of the Brain through MRI Analysis and Naive Bayes Classifier},<br />booktitle={Proceedings of the 2nd International Conference on Image Processing and Vision Engineering - Volume 1: IMPROVE,},<br />year={2022},<br />pages={26-33},<br />publisher={SciTePress},<br />organization={INSTICC},<br />doi={10.5220/0011056800003209},<br />isbn={978-989-758-563-0},<br />}</p> <br/> <h4 style="margin:0;">in EndNote Style</h4> <p style="margin:0;">TY - CONF <br /><br />JO - Proceedings of the 2nd International Conference on Image Processing and Vision Engineering - Volume 1: IMPROVE,<br />TI - Detecting Tourette鈥檚 Syndrome in Anatomical Regions of the Brain through MRI Analysis and Naive Bayes Classifier<br />SN - 978-989-758-563-0<br />AU - Costa De Barros M. <br />AU - Duarte K. <br />AU - Lee W. <br />AU - Hsu C. <br />AU - Garcia De Carvalho M. <br />PY - 2022<br />SP - 26<br />EP - 33<br />DO - 10.5220/0011056800003209<br /></p> <br/> </article> </body> </html>