<doi_batch xmlns="http://www.crossref.org/schema/4.4.0" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" version="4.4.0"><head><doi_batch_id>b7439463-5634-4505-8c48-53d05a6c0da3</doi_batch_id><timestamp>20210126134616069</timestamp><depositor><depositor_name>wsea</depositor_name><email_address>mdt@crossref.org</email_address></depositor><registrant>MDT Deposit</registrant></head><body><journal><journal_metadata language="en"><full_title>WSEAS TRANSACTIONS ON SIGNAL PROCESSING</full_title><issn media_type="print">1790-5052</issn><doi_data><doi>10.37394/232014</doi><resource>http://wseas.org/wseas/cms.action?id=4062</resource></doi_data></journal_metadata><journal_issue><publication_date media_type="online"><month>3</month><day>5</day><year>2020</year></publication_date><publication_date media_type="print"><month>3</month><day>5</day><year>2020</year></publication_date><journal_volume><volume>16</volume><doi_data><doi>10.37394/232014.2020.16</doi><resource>http://wseas.org/wseas/cms.action?id=23193</resource></doi_data></journal_volume></journal_issue><journal_article language="en"><titles><title>Active Contours with Term of Smoothing: Application in Medical Imaging</title></titles><contributors><person_name sequence="first" contributor_role="author"><given_name>Riad</given_name><surname>Dib</surname><affiliation>Akli Mohand Oulhadj University, Bouira, Algeria</affiliation></person_name></contributors><jats:abstract xmlns:jats="http://www.ncbi.nlm.nih.gov/JATS1"><jats:p>The segmentation of images is to group the different pixels of the image into classes, each classgrouping similar pixels in the sense of a given criteria. The segmentation of images is, in general, verydifficult to achieve because the natural images are various, complex and the way of perceiving them variesaccording to the individuals. Because of this difficulty, several approaches have been proposed. Theapproach addressed in this article is the technique of active contours. We focus on implicit active contours;we propose an improvement for the model of Chan&amp;Vese. The application of our method on medicalimages shown they can be a tool for the image segmentation, and thus provide an aid to diagnosis for thepractitioner.</jats:p></jats:abstract><publication_date media_type="online"><month>8</month><day>27</day><year>2020</year></publication_date><publication_date media_type="print"><month>8</month><day>27</day><year>2020</year></publication_date><pages><first_page>108</first_page><last_page>117</last_page></pages><ai:program xmlns:ai="http://www.crossref.org/AccessIndicators.xsd" name="AccessIndicators"><ai:free_to_read start_date="2020-08-27"/><ai:license_ref applies_to="am" start_date="2020-08-27">https://www.wseas.org/multimedia/journals/signal/2020/a265114-079.pdf</ai:license_ref></ai:program><archive_locations><archive name="Portico"/></archive_locations><doi_data><doi>10.37394/232014.2020.16.13</doi><resource>https://www.wseas.org/multimedia/journals/signal/2020/a265114-079.pdf</resource></doi_data><citation_list><citation key="ref0"><doi>10.1109/iccv.1995.466871</doi><unstructured_citation>V. 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