Repository logo
 
Publication

Visualizing Structures in Confocal Microscopy Datasets Through Clusterization: A Case Study on Bile Ducts

2019Conference object Open access
Simple item page
dc.contributor.authorBeltran, Lizeth A.C.
dc.contributor.authorCruz, Carolina U.
dc.contributor.authorSantos, Jorge Luiz dos
dc.contributor.authorShivakumar, Pranavkumar
dc.contributor.authorBezerra, Jorge
dc.contributor.authorFreitas, Carla M.D.S.
dc.date.accessioned2020-02-05T12:21:37Z
dc.date.available2020-02-05T12:21:37Z
dc.date.issued2019
dc.descriptionAiming at a better result from previous works, we employed some heuristics found in the literature to determine the appropriate parameters for the clustering. We proposed our methodology by adding some steps to be performed before the clustering phase: one step for pre-processing the volumetric dataset and another to analyzing candidate features to guide the clustering. In this latter aspect, we provide an interesting contribution: we have explored the gradient magnitude as a feature that allowed to extract relevant information from the density-based spatial clustering. Besides the fact that DBSCAN allows easy detection of noise points, an interesting result for both datasets was that the first and largest cluster found as significant for the visualization represents the structure of interest. In the red channel, this cluster represents the most prominent vessels, while in the green channel, the peribiliary glands were made more evident.pt_PT
dc.description.abstractAbstract—Three-dimensional datasets from biological tissues have increased with the evolution of confocal microscopy. Hepatology researchers have used confocal microscopy for investigating the microanatomy of bile ducts. Bile ducts are complex tubular tissues consisting of many juxtaposed microstructures with distinct characteristics. Since confocal images are difficult to segment because of the noise introduced during the specimen preparation, traditional quantitative analyses used in medical datasets are difficult to perform on confocal microscopy data and require extensive user intervention. Thus, the visual exploration and analysis of bile ducts pose a challenge in hepatology research, requiring different methods. This paper investigates the application of unsupervised machine learning to extract relevant structures from confocal microscopy datasets representing bile ducts. Our approach consists of pre-processing, clustering, and 3D visualization. For clustering, we explore the density-based spatial clustering for applications with noise (DBSCAN) algorithm, using gradient information for guiding the clustering. We obtained a better visualization of the most prominent vessels and internal structures.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1109/CBMS.2019.00086pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.6/9025
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.subjectConfocal microscopy datapt_PT
dc.subjectImage processingpt_PT
dc.subjectDB-scan clusteringpt_PT
dc.subjectVolumetric visualizationpt_PT
dc.titleVisualizing Structures in Confocal Microscopy Datasets Through Clusterization: A Case Study on Bile Ductspt_PT
dc.typeconference object
dspace.entity.typePublication
oaire.citation.endPage410pt_PT
oaire.citation.startPage405pt_PT
oaire.citation.title2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS)pt_PT
person.familyNamedos Santos
person.givenNameJorge Luiz
person.identifier.orcid0000-0002-3839-0222
rcaap.rightsopenAccesspt_PT
rcaap.typeconferenceObjectpt_PT
relation.isAuthorOfPublicatione2e81f33-a7b7-448c-a479-81afd7ca3378
relation.isAuthorOfPublication.latestForDiscoverye2e81f33-a7b7-448c-a479-81afd7ca3378

Files

Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
Visualizing Structures in Confocal Microscopy Datasets Through Clusterization A Case Study on Bile Ducts.pdf
Size:
457.34 KB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

FCS - DCM | Documentos por Auto-Depósito