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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPBW4/35S9PC8
Repositorysid.inpe.br/sibgrapi@80/2009/08.18.15.46
Last Update2009:08.18.15.46.19 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi@80/2009/08.18.15.46.20
Metadata Last Update2022:06.14.00.14.04 (UTC) administrator
DOI10.1109/SIBGRAPI.2009.25
Citation KeyRamirezVillegasLamERami:2009:MiDeMa
TitleMicrocalcification detection in mammograms using difference of gaussians filters and a hybrid feedforward-Kohonen neural network
FormatPrinted, On-line.
Year2009
Access Date2024, May 03
Number of Files1
Size936 KiB
2. Context
Author1 Ramirez Villegas, Juan Felipe
2 Lam Espinosa, Eric
3 Ramirez Moreno, David Fernando
Affiliation1 Universidad Autonoma de Occidente
2 Universidad Autonoma de Occidente
3 Universidad Autonoma de Occidente
EditorNonato, Luis Gustavo
Scharcanski, Jacob
e-Mail Addressjuanfelipe.rv@gmail.com
Conference NameBrazilian Symposium on Computer Graphics and Image Processing, 22 (SIBGRAPI)
Conference LocationRio de Janeiro, RJ, Brazil
Date11-14 Oct. 2009
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2010-08-28 20:03:27 :: juanfelipe.rv@gmail.com -> administrator ::
2022-06-14 00:14:04 :: administrator -> :: 2009
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
KeywordsMicrocalcification
mammogram
difference of gaussians filters
artificial neural networks
hard limit function
self-organizing map
AbstractThis work develops a microcalcifications detection system in mammograms by using difference of Gaussians filters (DoG) and artificial neural networks (ANN). The digital image processing proposed show the basic wavelet-based behavior of DoG as a mother function frequently used in several vision tasks, and in this case, used in order to enhance the microcalcifications traces in standard mammograms and further to achieve its detection via ANN. In order to achieve this, a segmentation algorithm is implemented for reaching a threshold in already processed images, and finally, the resultant information is given to the ANN. The neural network used to perform the detection is a hybrid feedforward-Kohonen one, implemented with a hard-limit transfer function in the first layer and a self-organizing map (SOM) responsible for microcalcifications topologic adjustment in the second layer. Basically, this clustering method gave us a robust solution of the problem and the detection was performed efficiently. There are no considerations relative to morphologic analysis of microcalcifications for diagnosis in this work.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2009 > Microcalcification detection in...
Arrangement 2urlib.net > SDLA > Fonds > Full Index > Microcalcification detection in...
doc Directory Contentaccess
source Directory Contentthere are no files
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPBW4/35S9PC8
zipped data URLhttp://urlib.net/zip/8JMKD3MGPBW4/35S9PC8
Languageen
Target FilePID949710.pdf
User Groupjuanfelipe.rv@gmail.com
Visibilityshown
5. Allied materials
Next Higher Units8JMKD3MGPEW34M/46SJQ2S
8JMKD3MGPEW34M/4742MCS
Citing Item Listsid.inpe.br/sibgrapi/2022/05.14.19.43 3
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination documentstage edition electronicmailaddress group isbn issn label lineage mark mirrorrepository nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url volume


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