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Feature extraction and classification to diagnose hypoxic-ischemic encephalopathy patients by using susceptibility-weighted MRI images

Feature extraction and classification to diagnose hypoxic-ischemic encephalopathy patients by using susceptibility-weighted MRI images
Feature extraction and classification to diagnose hypoxic-ischemic encephalopathy patients by using susceptibility-weighted MRI images
In this paper. a method is presented to enable automatic classification of the degree of abnormality of susceptibility-weighted images (SWI) acquired from babies with hypoxic-ischemic encephalopathy (HIE), in order to more accurately predict eventual cognitive and motor outcomes in these infants. SWI images highlight the cerebral venous vasculature and can reflect abnormalities in blood flow and oxygenation, which may be linked to adverse outcomes. A qualitative score based on MRI analyses is assigned to SWIs by specialists to determine the severity of abnormality in an HIE patient. The method allows the detection of image ridges, representing the vessels in SWIs, and the histogram of the ridges grey scales. A curve with only four parameters is fitted to the histograms. These parameters are then used to estimate the SWI abnormality score. The images are classified by using a kNN- and multiple SVM classifiers based on the parameters of the fitting curves. The algorithm is tested on an SWI-MRI dataset consisting of 10 healthy infants and 51 infants with HIE with a range of SWI abnormality scores between 1 and 7. The accuracy of classifying babies with HIE vs. those without (ie: healthy controls) using our algorithm with a leave-one-out strategy is measured as 91.38%. Our method is fast and could increase the prognostic value of these scans, thereby improving management of the condition, as well as elucidating the disease mechanisms of HIE.
1865-0929
Springer
Wu, Sisi
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Mahmoodi, Sasan
91ca8da4-95dc-4c1e-ac0e-f2c08d6ac7cf
Darker, Angela
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Vollmer, Brigitte
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Lewis, Emma
932d0fdf-d3d5-40f0-9ed2-7a4ca1fd3e74
Liljeroth, Maria
30815318-3434-4a70-9010-957799172516
Wu, Sisi
b07bb268-e094-45f9-a190-d066c37f22af
Mahmoodi, Sasan
91ca8da4-95dc-4c1e-ac0e-f2c08d6ac7cf
Darker, Angela
0d0f834d-761e-4bc2-b9f8-49c624de6514
Vollmer, Brigitte
044f8b55-ba36-4fb2-8e7e-756ab77653ba
Lewis, Emma
932d0fdf-d3d5-40f0-9ed2-7a4ca1fd3e74
Liljeroth, Maria
30815318-3434-4a70-9010-957799172516

Wu, Sisi, Mahmoodi, Sasan, Darker, Angela, Vollmer, Brigitte, Lewis, Emma and Liljeroth, Maria (2017) Feature extraction and classification to diagnose hypoxic-ischemic encephalopathy patients by using susceptibility-weighted MRI images. In Medical Image Understanding and Analysis. MIUA 2017. vol. 723, Springer. 11 pp . (doi:10.1007/978-3-319-60964-5_46).

Record type: Conference or Workshop Item (Paper)

Abstract

In this paper. a method is presented to enable automatic classification of the degree of abnormality of susceptibility-weighted images (SWI) acquired from babies with hypoxic-ischemic encephalopathy (HIE), in order to more accurately predict eventual cognitive and motor outcomes in these infants. SWI images highlight the cerebral venous vasculature and can reflect abnormalities in blood flow and oxygenation, which may be linked to adverse outcomes. A qualitative score based on MRI analyses is assigned to SWIs by specialists to determine the severity of abnormality in an HIE patient. The method allows the detection of image ridges, representing the vessels in SWIs, and the histogram of the ridges grey scales. A curve with only four parameters is fitted to the histograms. These parameters are then used to estimate the SWI abnormality score. The images are classified by using a kNN- and multiple SVM classifiers based on the parameters of the fitting curves. The algorithm is tested on an SWI-MRI dataset consisting of 10 healthy infants and 51 infants with HIE with a range of SWI abnormality scores between 1 and 7. The accuracy of classifying babies with HIE vs. those without (ie: healthy controls) using our algorithm with a leave-one-out strategy is measured as 91.38%. Our method is fast and could increase the prognostic value of these scans, thereby improving management of the condition, as well as elucidating the disease mechanisms of HIE.

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MIUA_073 _Sisi - Accepted Manuscript
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Accepted/In Press date: 19 April 2017
e-pub ahead of print date: 22 June 2017
Additional Information: AM now added. Template needs adjusting to Chapter in conf proceedings.
Venue - Dates: Medical Image Understanding and Analysis, Edinburgh, United Kingdom, 2017-07-11 - 2017-07-13
Organisations: Vision, Learning and Control

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Local EPrints ID: 407840
URI: https://eprints.soton.ac.uk/id/eprint/407840
ISSN: 1865-0929
PURE UUID: b4891b12-f530-458a-ad18-6e51ff19577a
ORCID for Sisi Wu: ORCID iD orcid.org/0000-0003-4488-7259
ORCID for Brigitte Vollmer: ORCID iD orcid.org/0000-0003-4088-5336

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Date deposited: 27 Apr 2017 01:02
Last modified: 20 Jul 2019 00:46

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Contributors

Author: Sisi Wu ORCID iD
Author: Sasan Mahmoodi
Author: Angela Darker
Author: Emma Lewis
Author: Maria Liljeroth

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