| 1. Identity statement | |
| Reference Type | Conference Paper (Conference Proceedings) |
| Site | sibgrapi.sid.inpe.br |
| Holder Code | ibi 8JMKD3MGPEW34M/46T9EHH |
| Identifier | 8JMKD3MGPBW34M/3868QFL |
| Repository | sid.inpe.br/sibgrapi/2010/08.28.15.30 |
| Last Update | 2010:08.28.15.30.00 (UTC) administrator |
| Metadata Repository | sid.inpe.br/sibgrapi/2010/08.28.15.30.01 |
| Metadata Last Update | 2024:03.23.15.30.55 (UTC) administrator |
| DOI | 10.1109/SIBGRAPI.2010.51 |
| Citation Key | ZampirolliStraLorePaul:2010:ApGrAn |
| Title | Segmentation and classification of histological images - application of graph analysis and machine learning methods  |
| Format | Printed, On-line. |
| Year | 2010 |
| Access Date | 2024, May 01 |
| Number of Files | 1 |
| Size | 1134 KiB |
| 2. Context | |
| Author | 1 Zampirolli, Francisco de Assis 2 Stransky, Beatriz 3 Lorena, Ana Carolina 4 Paulon, Fábio Luis de Melo |
| Affiliation | 1 Universidade Federal do ABC 2 Universidade Federal do ABC 3 Universidade Federal do ABC 4 Universidade Federal do ABC |
| Editor | Bellon, Olga Esperança, Claudio |
| e-Mail Address | fzampirolli@gmail.com |
| Conference Name | Conference on Graphics, Patterns and Images, 23 (SIBGRAPI) |
| Conference Location | Gramado, RS, Brazil |
| Date | 30 Aug.-3 Sep. 2010 |
| Publisher | IEEE Computer Society |
| Publisher City | Los Alamitos |
| Book Title | Proceedings |
| Tertiary Type | Full Paper |
| History (UTC) | 2010-10-01 04:19:37 :: fzampirolli@gmail.com -> administrator :: 2010 2024-03-23 15:30:55 :: administrator -> :: 2010 |
| 3. Content and structure | |
| Is the master or a copy? | is the master |
| Content Stage | completed |
| Transferable | 1 |
| Version Type | finaldraft |
| Keywords | image analysis mathematical morphology graph analysis machine learning tissue |
| Abstract | The characterization and quantitative description of histological images is not a simple problem. To reach a final diagnosis, usually the specialist relies on the analysis of characteristics easily observed, such as cells size, shape, staining and texture, but also depends on the hidden information of tissue localization, physiological and pathological mechanisms, clinical aspects, or other etiological agents. In this paper, Mathematical Morphology (MM) and Machine Learning (ML) methods were applied to characterize and classify histological images. MM techniques were employed for image analysis. The measurements obtained from image and graph analysis were fed into Machine Learning algorithms, which were designed and developed to automatically learn to recognize complex patterns and make intelligent decisions based on data. Specifically, a linear Support Vector Machine (SVM) was used to evaluate the discriminatory power of the used measures. The results show that the methodology was successful in characterizing and classifying the differences between the architectural organization of epithelial and adipose tissues. We believe that this approach can be also applied to classify and help. |
| Arrangement 1 | MM > Segmentation and classification... |
| Arrangement 2 | urlib.net > SDLA > Fonds > SIBGRAPI 2010 > Segmentation and classification... |
| Arrangement 3 | urlib.net > SDLA > Fonds > Full Index > Segmentation and classification... |
| doc Directory Content | access |
| source Directory Content | there are no files |
| agreement Directory Content | there are no files |
| 4. Conditions of access and use | |
| data URL | http://urlib.net/ibi/8JMKD3MGPBW34M/3868QFL |
| zipped data URL | http://urlib.net/zip/8JMKD3MGPBW34M/3868QFL |
| Language | en |
| Target File | article_sibgrapi_v8.pdf |
| User Group | fzampirolli@gmail.com |
| Visibility | shown |
| 5. Allied materials | |
| Mirror Repository | sid.inpe.br/banon/2001/03.30.15.38.24 |
| Next Higher Units | 8JMKD3MGPCW/4AUUH9L 8JMKD3MGPEW34M/46SJT6B 8JMKD3MGPEW34M/4742MCS |
| Citing Item List | sid.inpe.br/sibgrapi/2022/05.14.20.21 4 |
| Host Collection | sid.inpe.br/banon/2001/03.30.15.38 |
| 6. Notes | |
| Empty Fields | archivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination documentstage 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 |