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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPAW/3M5GCSH
Repositorysid.inpe.br/sibgrapi/2016/07.22.04.46
Last Update2016:07.22.04.46.39 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2016/07.22.04.46.40
Metadata Last Update2022:06.14.00.08.32 (UTC) administrator
DOI10.1109/SIBGRAPI.2016.014
Citation KeyOliveiraSanMelRêgBat:2016:InThDe
TitleInformation Theory-based Detection of Noisy Bit Planes in Medical Images
FormatOn-line
Year2016
Access Date2024, May 02
Number of Files1
Size1008 KiB
2. Context
Author1 Oliveira, Hugo Neves de
2 Santos, Jefersson Alex dos
3 Melo, Matheus Cordeiro de
4 Rêgo, Thaís Gaudencio do
5 Batista, Leonardo Vidal
Affiliation1 Universidade Federal de Minas Gerais
2 Universidade Federal de Minas Gerais
3 Universidade Federal da Paraíba
4 Universidade Federal da Paraíba
5 Universidade Federal da Paraíba
EditorAliaga, Daniel G.
Davis, Larry S.
Farias, Ricardo C.
Fernandes, Leandro A. F.
Gibson, Stuart J.
Giraldi, Gilson A.
Gois, João Paulo
Maciel, Anderson
Menotti, David
Miranda, Paulo A. V.
Musse, Soraia
Namikawa, Laercio
Pamplona, Mauricio
Papa, João Paulo
Santos, Jefersson dos
Schwartz, William Robson
Thomaz, Carlos E.
e-Mail Addressoliveirahugo@dcc.ufmg.br
Conference NameConference on Graphics, Patterns and Images, 29 (SIBGRAPI)
Conference LocationSão José dos Campos, SP, Brazil
Date4-7 Oct. 2016
PublisherIEEE Computer Society´s Conference Publishing Services
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2016-07-22 04:46:40 :: oliveirahugo@dcc.ufmg.br -> administrator ::
2016-10-05 14:49:15 :: administrator -> oliveirahugo@dcc.ufmg.br :: 2016
2016-10-13 03:24:03 :: oliveirahugo@dcc.ufmg.br -> administrator :: 2016
2022-06-14 00:08:32 :: administrator -> :: 2016
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
Keywordsnoise detection
mammogram classification
information theory
data compression
AbstractMammographic Computer-Aided Diagnosis systems are applications designed to assist radiologists in diagnosis of malignancy in mammographic findings. Most methods described in the literature do not perform a proper preprocessing step in mammographic images prior to classification, which can generate inconsistent results due to the potentially large amount of noise in medical images. This paper proposes a new method based on Information Theory and Data Compression for detection of random noise in image bit planes. In order to validate the efficiency of the proposed noise removal method, we used Machine Learning algorithms to classify mammographic findings from the Digital Database for Screening Mammography. Results using texture features indicate that a reduction in the radiometric resolution of 4 or 5 bit planes in digitized screen film mammographic images result in a better classification performance.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2016 > Information Theory-based Detection...
Arrangement 2urlib.net > SDLA > Fonds > Full Index > Information Theory-based Detection...
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Content
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPAW/3M5GCSH
zipped data URLhttp://urlib.net/zip/8JMKD3MGPAW/3M5GCSH
Languageen
Target FileInformation_Theory_based_Detection_of_Noisy_BitPlanes_in_Medical_Images_Final.pdf
User Groupoliveirahugo@dcc.ufmg.br
Visibilityshown
Update Permissionnot transferred
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPAW/3M2D4LP
8JMKD3MGPEW34M/4742MCS
Citing Item Listsid.inpe.br/sibgrapi/2016/07.02.23.50 6
sid.inpe.br/sibgrapi/2022/06.10.21.49 1
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination edition electronicmailaddress group isbn issn label lineage mark 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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