Identity statement area
Reference TypeConference Paper (Conference Proceedings)
Last Update2012:
Metadata Last Update2020: administrator
Citation KeyMirandaBarrSoarFeli:2012:StAnHi
TitleStructural Analysis of Histological Images to Aid Diagnosis of Cervical Cancer
FormatDVD, On-line.
Access Date2021, Jan. 24
Number of Files1
Size1678 KiB
Context area
Author1 Miranda, Gisele Helena Barboni
2 Barrera, Junior
3 Soares, Edson Garcia
4 Felipe, Joaquim Cezar
Affiliation1 Department of Computing and Mathematics, Faculty of Philosophy, Sciences and Languages of Ribeirão Preto, USP
2 Department of Computer Science, Institute of Mathematics and Statistics, USP
3 Department of Pathology, Faculty of Medicine of Ribeirão Preto, USP
4 Department of Computing and Mathematics, Faculty of Philosophy, Sciences and Languages of Ribeirão Preto, USP
EditorFreitas, Carla Maria Dal Sasso
Sarkar, Sudeep
Scopigno, Roberto
Silva, Luciano
Conference NameConference on Graphics, Patterns and Images, 25 (SIBGRAPI)
Conference LocationOuro Preto
DateAug. 22-25, 2012
Book TitleProceedings
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Tertiary TypeFull Paper
History2012-09-20 16:45:34 :: -> administrator :: 2012
2020-02-19 02:18:29 :: administrator -> :: 2012
Content and structure area
Is the master or a copy?is the master
Content Stagecompleted
Content TypeExternal Contribution
KeywordsCervical Intraepithelial Neoplasia (CIN), neighborhood graphs, medical image processing, computer-aided diagnosis.
AbstractThe use of computational techniques in the processing of histopathological images allows the study of the structural organization of tissues and their pathological changes. The overall objective of this work includes the proposal, the implementation and the evaluation of a methodology for the analysis of cervical intraepithelial neoplasia (CIN) from histopathological images. For this pourpose, a pipeline of morphological operators were implemented for the segmentation of cell nuclei and the Delaunay Triangulation were used in order to represent the tissue architecture. Also, clustering algorithms and graph morphology were used to automatically obtain the boundary between the histological layers of the epithelial tissue. Similarity criteria and adjacency relations between the triangles of the network were explored. The proposed method was evaluated concerning the detection of the presence of lesions in the tissue as well as the their malignancy grading.
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