Describing Pulmonary Nodules Using 3D Clustering
Introduction. Determining the tumor (nodule) characteristics in terms of the shape, location, and type is an essential step after nodule detection in medical images for selecting the appropriate clinical intervention by radiologists. Computer-aided detection (CAD) systems efficiently succeeded in th...
| Main Authors: | , , |
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| Format: | Article |
| Language: | Russian |
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Don State Technical University
2022-10-01
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| Series: | Advanced Engineering Research |
| Subjects: | |
| Online Access: | https://www.vestnik-donstu.ru/jour/article/view/1912 |
| _version_ | 1797881330507186176 |
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| author | Amera Al-Funjan Farid Meziane Rob Aspin |
| author_facet | Amera Al-Funjan Farid Meziane Rob Aspin |
| author_sort | Amera Al-Funjan |
| collection | DOAJ |
| description | Introduction. Determining the tumor (nodule) characteristics in terms of the shape, location, and type is an essential step after nodule detection in medical images for selecting the appropriate clinical intervention by radiologists. Computer-aided detection (CAD) systems efficiently succeeded in the nodule detection by 2D processing of computed tomography (CT)-scan lung images; however, the nodule (tumor) description in more detail is still a big challenge that faces these systems.Materials and Methods. In this paper, the 3D clustering is carried out on volumetric CT-scan images containing the nodule and its structures to describe the nodule progress through the consecutive slices of the lung in CT images.Results. This paper combines algorithms to cluster and define nodule’s features in 3D visualization. Applying some 3D functions to the objects, clustered using the K-means technique of CT lung images, provides a 3D visual exploration of the nodule shape and location. This study mainly focuses on clustering in 3D to discover complex information for a case missed in the radiologist’s report. In addition, the 3D-Density-based spatial clustering of applications with noise (DBSCAN) method and another 3D application (plotly) have been applied to evaluate the proposed system in this work. The proposed method has discovered a complicated case in data and automatically provides information about the nodule types (spherical, juxta-pleural, and pleural-tail). The algorithm is validated on the standard data consisting of the lung computed tomography scans with nodules greater and less than 3mm in size.Discussion and Conclusions. Based on the proposed model, it is possible to cluster lung nodules in volumetric CT scan and determine a set of characteristics such as the shape, location and type. |
| first_indexed | 2024-04-10T03:17:04Z |
| format | Article |
| id | doaj.art-f821bc8e2dd043c994b33420e73e142c |
| institution | Directory Open Access Journal |
| issn | 2687-1653 |
| language | Russian |
| last_indexed | 2024-04-10T03:17:04Z |
| publishDate | 2022-10-01 |
| publisher | Don State Technical University |
| record_format | Article |
| series | Advanced Engineering Research |
| spelling | doaj.art-f821bc8e2dd043c994b33420e73e142c2023-03-13T07:31:30ZrusDon State Technical UniversityAdvanced Engineering Research2687-16532022-10-0122326127110.23947/2687-1653-2022-22-3-261-2711571Describing Pulmonary Nodules Using 3D ClusteringAmera Al-Funjan0Farid Meziane1Rob Aspin2Babylon UniversityUniversity of DerbyManchester Metropolitan UniversityIntroduction. Determining the tumor (nodule) characteristics in terms of the shape, location, and type is an essential step after nodule detection in medical images for selecting the appropriate clinical intervention by radiologists. Computer-aided detection (CAD) systems efficiently succeeded in the nodule detection by 2D processing of computed tomography (CT)-scan lung images; however, the nodule (tumor) description in more detail is still a big challenge that faces these systems.Materials and Methods. In this paper, the 3D clustering is carried out on volumetric CT-scan images containing the nodule and its structures to describe the nodule progress through the consecutive slices of the lung in CT images.Results. This paper combines algorithms to cluster and define nodule’s features in 3D visualization. Applying some 3D functions to the objects, clustered using the K-means technique of CT lung images, provides a 3D visual exploration of the nodule shape and location. This study mainly focuses on clustering in 3D to discover complex information for a case missed in the radiologist’s report. In addition, the 3D-Density-based spatial clustering of applications with noise (DBSCAN) method and another 3D application (plotly) have been applied to evaluate the proposed system in this work. The proposed method has discovered a complicated case in data and automatically provides information about the nodule types (spherical, juxta-pleural, and pleural-tail). The algorithm is validated on the standard data consisting of the lung computed tomography scans with nodules greater and less than 3mm in size.Discussion and Conclusions. Based on the proposed model, it is possible to cluster lung nodules in volumetric CT scan and determine a set of characteristics such as the shape, location and type.https://www.vestnik-donstu.ru/jour/article/view/1912автоматизированная 3d-кластеризациякт легкихописание характеристик узлов |
| spellingShingle | Amera Al-Funjan Farid Meziane Rob Aspin Describing Pulmonary Nodules Using 3D Clustering Advanced Engineering Research автоматизированная 3d-кластеризация кт легких описание характеристик узлов |
| title | Describing Pulmonary Nodules Using 3D Clustering |
| title_full | Describing Pulmonary Nodules Using 3D Clustering |
| title_fullStr | Describing Pulmonary Nodules Using 3D Clustering |
| title_full_unstemmed | Describing Pulmonary Nodules Using 3D Clustering |
| title_short | Describing Pulmonary Nodules Using 3D Clustering |
| title_sort | describing pulmonary nodules using 3d clustering |
| topic | автоматизированная 3d-кластеризация кт легких описание характеристик узлов |
| url | https://www.vestnik-donstu.ru/jour/article/view/1912 |
| work_keys_str_mv | AT ameraalfunjan describingpulmonarynodulesusing3dclustering AT faridmeziane describingpulmonarynodulesusing3dclustering AT robaspin describingpulmonarynodulesusing3dclustering |