donstuAdvanced Engineering Research (Rostov-on-Don)Advanced Engineering Research (Rostov-on-Don)2687-1653Don State Technical University10.23947/2687-1653-2022-22-3-261-271donstu-1912Research ArticleINFORMATION TECHNOLOGY, COMPUTER SCIENCE AND MANAGEMENTИНФОРМАТИКА, ВЫЧИСЛИТЕЛЬНАЯ ТЕХНИКА И УПРАВЛЕНИЕDescribing Pulmonary Nodules Using 3D ClusteringОписание легочных узлов с использованием трехмерной кластеризацииhttps://orcid.org/0000-0002-6889-6670Al-FunjanAmeraAl-FunjanAmera

Al-Funjan, Amera, Lecturer in Mathematics Department, College of Pure Sciences, Ph.D

ResearcherID

PO Box 4 Hilla City, Babylon, 51001

 

 

Al-Funjan, Amera, Lecturer in Mathematics Department, College of Pure Sciences, Ph.D

ResearcherID

PO Box 4 Hilla City, Babylon, 51001

amera.alfunjan@uobabylon.edu.iqhttps://orcid.org/0000-0001-9811-6914MezianeFaridMezianeFarid

Meziane, Farid, College of Science and Engineering, School of Computing and Engineering, Ph.D

ScopusID

DE22 1GB, Derby

 

Meziane, Farid, College of Science and Engineering, School of Computing and Engineering, Ph.D

ScopusID

DE22 1GB, Derby

f.meziane@derby.ac.ukhttps://orcid.org/0000-0002-2202-1326AspinRobAspinRob

Aspin, Rob, Deputy Head, Ph.D

ScopusID

M15 6BH, Manchester

Aspin, Rob, Deputy Head, Ph.D

ScopusID

M15 6BH, Manchester

R.Aspin@mmu.ac.ukBabylon UniversityИракBabylon UniversityIraqUniversity of DerbyВеликобританияUniversity of DerbyUnited KingdomManchester Metropolitan UniversityВеликобританияManchester Metropolitan UniversityUnited Kingdom202213102022223261271Copyright © Al-Funjan A., Meziane F., Aspin R., 20222022Al-Funjan A., Meziane F., Aspin R.Al-Funjan A., Meziane F., Aspin R.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://www.vestnik-donstu.ru/jour/article/view/1912Introduction

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

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

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.

Введение

Введение. После обнаружения на медицинских снимках узла (опухоли) необходимо определить его форму, локализацию и тип. Это важно для выбора вида клинического вмешательства и других аспектов работырадиологов. Системы компьютерного обнаружения эффективно выявляют узлы с помощью 2D-обработки изображений компьютерной томографии (КТ) легких. Однако более подробное описание узла (опухоли) по-прежнему представляет собой большую проблему.

Материалы и методы

Материалы и методы. В рамках данной работы трехмерная кластеризация выполнялась на объемных КТ изображениях, которые дают представление об узле и его структуре. Эти материалы использовались для описания развития узла по последовательным срезам легкого.

Результаты исследований

Результаты исследований. Объединены алгоритмы кластеризации и определения характеристик узлов в 3D-визуализации. Некоторые трехмерные функции применили к объектам, сгруппированным методом K-средних КТ изображений легких. Такой подход обеспечивает визуальное изучение трехмерной формы и местоположения узла. Данное исследование в основном сосредоточено на кластеризации в 3D с целью получения сложной информации, пропущенной в отчете рентгенолога. Кроме того, для оценки предлагаемой системы в работе применили 3D плотностный алгоритм кластеризации пространственных данных с присутствием шума и еще одно 3D приложение – график. Предлагаемый метод обнаружил сложный случай и автоматически предоставил информацию о типах узлов (шаровидный, юкстаплевральный и плеврально-хвостовой). Алгоритм проверен на стандартных данных, состоявших из сканов компьютерной томографии легких с узлами размером более и менее 3 мм.

Обсуждение и заключения

Обсуждение и заключения. На основе предложенной модели можно кластеризовать узлы легких при объемной КТ и определять набор таких характеристик, как форма, расположение и тип.

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The authors declare that there are no conflicts of interest present.