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Diagnostics 2018, 8(3), 56; https://doi.org/10.3390/diagnostics8030056

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

1
Centre for Artificial Intelligence, Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW 2007, Australia
2
The Tumour Bank, The Children’s Cancer Research Unit, The Kids Research Institute, The Children’s Hospital at Westmead, Locked Bag 4001, Westmead, NSW 2145, Australia
3
Department of Histopathology, Auckland City Hospital, Auckland 1023, New Zealand
4
Department of Molecular Medicine and Pathology, Faculty of Medical and Health Sciences, University of Auckland, Auckland 1142, New Zealand
5
Department of Pathology, Southmead Hospital, Bristol BS10 5NB, UK
6
Department of Cellular Pathology, Pathology Science Building, Southmead Hospital, Bristol BS10 5NB, UK
*
Author to whom correspondence should be addressed.
Received: 20 July 2018 / Revised: 15 August 2018 / Accepted: 23 August 2018 / Published: 28 August 2018
(This article belongs to the Special Issue Computer-Aided Diagnosis and Characterization of Diseases)
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Abstract

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. View Full-Text
Keywords: neuroblastoma; histological images; Scale Invariant Feature Transform (SIFT); feature encoding neuroblastoma; histological images; Scale Invariant Feature Transform (SIFT); feature encoding
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Gheisari, S.; Catchpoole, D.R.; Charlton, A.; Melegh, Z.; Gradhand, E.; Kennedy, P.J. Computer Aided Classification of Neuroblastoma Histological Images Using Scale Invariant Feature Transform with Feature Encoding. Diagnostics 2018, 8, 56.

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