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Computer Aided Classification of Neuroblastoma Histological Images Using Scale Invariant Feature Transform with Feature Encoding

Neuroblastoma is the most common extracranial solid malignancy in early childhood. Optimal management of neuroblastoma depends on many factors, including histopathological classification. Although histopathology study is considered the gold standard for classification of neuroblastoma histological i...

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Autores principales: Gheisari, Soheila, Catchpoole, Daniel R., Charlton, Amanda, Melegh, Zsombor, Gradhand, Elise, Kennedy, Paul J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6165255/
https://www.ncbi.nlm.nih.gov/pubmed/30154334
http://dx.doi.org/10.3390/diagnostics8030056
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author Gheisari, Soheila
Catchpoole, Daniel R.
Charlton, Amanda
Melegh, Zsombor
Gradhand, Elise
Kennedy, Paul J.
author_facet Gheisari, Soheila
Catchpoole, Daniel R.
Charlton, Amanda
Melegh, Zsombor
Gradhand, Elise
Kennedy, Paul J.
author_sort Gheisari, Soheila
collection PubMed
description Neuroblastoma is the most common extracranial solid malignancy in early childhood. Optimal management of neuroblastoma depends on many factors, including histopathological classification. Although histopathology study is considered the gold standard for classification of neuroblastoma histological images, computers can help to extract many more features some of which may not be recognizable by human eyes. This paper, proposes a combination of Scale Invariant Feature Transform with feature encoding algorithm to extract highly discriminative features. Then, distinctive image features are classified by Support Vector Machine classifier into five clinically relevant classes. The advantage of our model is extracting features which are more robust to scale variation compared to the Patched Completed Local Binary Pattern and Completed Local Binary Pattern methods. We gathered a database of 1043 histologic images of neuroblastic tumours classified into five subtypes. Our approach identified features that outperformed the state-of-the-art on both our neuroblastoma dataset and a benchmark breast cancer dataset. Our method shows promise for classification of neuroblastoma histological images.
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spelling pubmed-61652552018-10-11 Computer Aided Classification of Neuroblastoma Histological Images Using Scale Invariant Feature Transform with Feature Encoding Gheisari, Soheila Catchpoole, Daniel R. Charlton, Amanda Melegh, Zsombor Gradhand, Elise Kennedy, Paul J. Diagnostics (Basel) Article Neuroblastoma is the most common extracranial solid malignancy in early childhood. Optimal management of neuroblastoma depends on many factors, including histopathological classification. Although histopathology study is considered the gold standard for classification of neuroblastoma histological images, computers can help to extract many more features some of which may not be recognizable by human eyes. This paper, proposes a combination of Scale Invariant Feature Transform with feature encoding algorithm to extract highly discriminative features. Then, distinctive image features are classified by Support Vector Machine classifier into five clinically relevant classes. The advantage of our model is extracting features which are more robust to scale variation compared to the Patched Completed Local Binary Pattern and Completed Local Binary Pattern methods. We gathered a database of 1043 histologic images of neuroblastic tumours classified into five subtypes. Our approach identified features that outperformed the state-of-the-art on both our neuroblastoma dataset and a benchmark breast cancer dataset. Our method shows promise for classification of neuroblastoma histological images. MDPI 2018-08-28 /pmc/articles/PMC6165255/ /pubmed/30154334 http://dx.doi.org/10.3390/diagnostics8030056 Text en © 2018 by the authors. 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/).
spellingShingle Article
Gheisari, Soheila
Catchpoole, Daniel R.
Charlton, Amanda
Melegh, Zsombor
Gradhand, Elise
Kennedy, Paul J.
Computer Aided Classification of Neuroblastoma Histological Images Using Scale Invariant Feature Transform with Feature Encoding
title Computer Aided Classification of Neuroblastoma Histological Images Using Scale Invariant Feature Transform with Feature Encoding
title_full Computer Aided Classification of Neuroblastoma Histological Images Using Scale Invariant Feature Transform with Feature Encoding
title_fullStr Computer Aided Classification of Neuroblastoma Histological Images Using Scale Invariant Feature Transform with Feature Encoding
title_full_unstemmed Computer Aided Classification of Neuroblastoma Histological Images Using Scale Invariant Feature Transform with Feature Encoding
title_short Computer Aided Classification of Neuroblastoma Histological Images Using Scale Invariant Feature Transform with Feature Encoding
title_sort computer aided classification of neuroblastoma histological images using scale invariant feature transform with feature encoding
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6165255/
https://www.ncbi.nlm.nih.gov/pubmed/30154334
http://dx.doi.org/10.3390/diagnostics8030056
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