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Lung Nodule Detection in CT Images Using Statistical and Shape-Based Features
The lung tumor is among the most detrimental kinds of malignancy. It has a high occurrence rate and a high death rate, as it is frequently diagnosed at the later stages. Computed Tomography (CT) scans are broadly used to distinguish the disease; computer aided systems are being created to analyze th...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8321000/ https://www.ncbi.nlm.nih.gov/pubmed/34460555 http://dx.doi.org/10.3390/jimaging6020006 |
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author | Khehrah, Noor Farid, Muhammad Shahid Bilal, Saira Khan, Muhammad Hassan |
author_facet | Khehrah, Noor Farid, Muhammad Shahid Bilal, Saira Khan, Muhammad Hassan |
author_sort | Khehrah, Noor |
collection | PubMed |
description | The lung tumor is among the most detrimental kinds of malignancy. It has a high occurrence rate and a high death rate, as it is frequently diagnosed at the later stages. Computed Tomography (CT) scans are broadly used to distinguish the disease; computer aided systems are being created to analyze the ailment at prior stages productively. In this paper, we present a fully automatic framework for nodule detection from CT images of lungs. A histogram of the grayscale CT image is computed to automatically isolate the lung locale from the foundation. The results are refined using morphological operators. The internal structures are then extracted from the parenchyma. A threshold-based technique is proposed to separate the candidate nodules from other structures, e.g., bronchioles and blood vessels. Different statistical and shape-based features are extracted for these nodule candidates to form nodule feature vectors which are classified using support vector machines. The proposed method is evaluated on a large lungs CT dataset collected from the Lung Image Database Consortium (LIDC). The proposed method achieved excellent results compared to similar existing methods; it achieves a sensitivity rate of 93.75%, which demonstrates its effectiveness. |
format | Online Article Text |
id | pubmed-8321000 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-83210002021-08-26 Lung Nodule Detection in CT Images Using Statistical and Shape-Based Features Khehrah, Noor Farid, Muhammad Shahid Bilal, Saira Khan, Muhammad Hassan J Imaging Article The lung tumor is among the most detrimental kinds of malignancy. It has a high occurrence rate and a high death rate, as it is frequently diagnosed at the later stages. Computed Tomography (CT) scans are broadly used to distinguish the disease; computer aided systems are being created to analyze the ailment at prior stages productively. In this paper, we present a fully automatic framework for nodule detection from CT images of lungs. A histogram of the grayscale CT image is computed to automatically isolate the lung locale from the foundation. The results are refined using morphological operators. The internal structures are then extracted from the parenchyma. A threshold-based technique is proposed to separate the candidate nodules from other structures, e.g., bronchioles and blood vessels. Different statistical and shape-based features are extracted for these nodule candidates to form nodule feature vectors which are classified using support vector machines. The proposed method is evaluated on a large lungs CT dataset collected from the Lung Image Database Consortium (LIDC). The proposed method achieved excellent results compared to similar existing methods; it achieves a sensitivity rate of 93.75%, which demonstrates its effectiveness. MDPI 2020-02-24 /pmc/articles/PMC8321000/ /pubmed/34460555 http://dx.doi.org/10.3390/jimaging6020006 Text en © 2020 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ). |
spellingShingle | Article Khehrah, Noor Farid, Muhammad Shahid Bilal, Saira Khan, Muhammad Hassan Lung Nodule Detection in CT Images Using Statistical and Shape-Based Features |
title | Lung Nodule Detection in CT Images Using Statistical and Shape-Based Features |
title_full | Lung Nodule Detection in CT Images Using Statistical and Shape-Based Features |
title_fullStr | Lung Nodule Detection in CT Images Using Statistical and Shape-Based Features |
title_full_unstemmed | Lung Nodule Detection in CT Images Using Statistical and Shape-Based Features |
title_short | Lung Nodule Detection in CT Images Using Statistical and Shape-Based Features |
title_sort | lung nodule detection in ct images using statistical and shape-based features |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8321000/ https://www.ncbi.nlm.nih.gov/pubmed/34460555 http://dx.doi.org/10.3390/jimaging6020006 |
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