The University of Southampton
University of Southampton Institutional Repository

Ridge detection and analysis of susceptibility-weighted magnetic resonance imaging in neonatal hypoxic-ischaemic encephalopathy

Ridge detection and analysis of susceptibility-weighted magnetic resonance imaging in neonatal hypoxic-ischaemic encephalopathy
Ridge detection and analysis of susceptibility-weighted magnetic resonance imaging in neonatal hypoxic-ischaemic encephalopathy
The purpose of this study is to develop a new automated system to classify susceptibility weighted images (SWI) obtained to evaluate neonatal hypoxic-ischaemic injury, by detecting and analyzing ridges within these images. SW images can depict abnormal cerebral venous contrast as a consequence of abnormal blood flow, perfusion and thus oxygenation in babies with HIE. In this research, a dataset of SWI-MRI images, acquired from 42 infants with HIE during the neonatal period, features are obtained based on ridge analysis of SW images including the width of blood vessels, the change in intensity of the veins’ pixels in comparison with neighboring pixels, the length of blood vessels and Hessian eigenvalues for ridges are extracted. Normalized histogram parameters in the single or combined features are used to classify SWIs by kNN and random forest classifiers. The mean and standard deviation of the classification accuracies are derived by randomly selecting 11 datasets ten times from those with normal neurological outcome (n=31) at age 24 months and those with abnormal neurological outcome (n=11), to avoids classification biases due to any imbalanced data. The feature vectors containing width, intensity, length and eigenvalue show a promising classification accuracy of 78.67% ± 2.58%. The features derived from the ridges of the blood vessels have a good discriminative power for prediction of neurological outcome in infants with neonatal HIE. We also employ Support Vector Regression (SVR) to predict the scores of motor and cognitive outcomes assessed 24 months after the birth. Our mean relative errors for cognitive and motor outcome scores are 0.113±0.13 and 0.109±0.067 respectively.
Hypoxic-Ischaemic Encephalopathy,, vessel intensity, SWI Ridges, neurological outcome
Springer
Tang, Zhen
a8a50743-212f-4fb2-84f1-805f0758a83e
Mahmoodi, Sasan
91ca8da4-95dc-4c1e-ac0e-f2c08d6ac7cf
Dasmahapatra, Srinandan
eb5fd76f-4335-4ae9-a88a-20b9e2b3f698
Darker, Angela
0d0f834d-761e-4bc2-b9f8-49c624de6514
Vollmer, Brigitte
044f8b55-ba36-4fb2-8e7e-756ab77653ba
Tang, Zhen
a8a50743-212f-4fb2-84f1-805f0758a83e
Mahmoodi, Sasan
91ca8da4-95dc-4c1e-ac0e-f2c08d6ac7cf
Dasmahapatra, Srinandan
eb5fd76f-4335-4ae9-a88a-20b9e2b3f698
Darker, Angela
0d0f834d-761e-4bc2-b9f8-49c624de6514
Vollmer, Brigitte
044f8b55-ba36-4fb2-8e7e-756ab77653ba

Tang, Zhen, Mahmoodi, Sasan, Dasmahapatra, Srinandan, Darker, Angela and Vollmer, Brigitte (2020) Ridge detection and analysis of susceptibility-weighted magnetic resonance imaging in neonatal hypoxic-ischaemic encephalopathy. In Proceedings of Medical Image Understanding and Analysis. Springer. 12 pp . (In Press)

Record type: Conference or Workshop Item (Paper)

Abstract

The purpose of this study is to develop a new automated system to classify susceptibility weighted images (SWI) obtained to evaluate neonatal hypoxic-ischaemic injury, by detecting and analyzing ridges within these images. SW images can depict abnormal cerebral venous contrast as a consequence of abnormal blood flow, perfusion and thus oxygenation in babies with HIE. In this research, a dataset of SWI-MRI images, acquired from 42 infants with HIE during the neonatal period, features are obtained based on ridge analysis of SW images including the width of blood vessels, the change in intensity of the veins’ pixels in comparison with neighboring pixels, the length of blood vessels and Hessian eigenvalues for ridges are extracted. Normalized histogram parameters in the single or combined features are used to classify SWIs by kNN and random forest classifiers. The mean and standard deviation of the classification accuracies are derived by randomly selecting 11 datasets ten times from those with normal neurological outcome (n=31) at age 24 months and those with abnormal neurological outcome (n=11), to avoids classification biases due to any imbalanced data. The feature vectors containing width, intensity, length and eigenvalue show a promising classification accuracy of 78.67% ± 2.58%. The features derived from the ridges of the blood vessels have a good discriminative power for prediction of neurological outcome in infants with neonatal HIE. We also employ Support Vector Regression (SVR) to predict the scores of motor and cognitive outcomes assessed 24 months after the birth. Our mean relative errors for cognitive and motor outcome scores are 0.113±0.13 and 0.109±0.067 respectively.

Text
final version
Download (575kB)

More information

Accepted/In Press date: 28 April 2020
Venue - Dates: Medical Image Understanding and Analysis, Oxford University, United Kingdom, 2020-07-15 - 2020-07-17
Keywords: Hypoxic-Ischaemic Encephalopathy,, vessel intensity, SWI Ridges, neurological outcome

Identifiers

Local EPrints ID: 440993
URI: http://eprints.soton.ac.uk/id/eprint/440993
PURE UUID: 5f4fa801-33ec-4ae8-8beb-d9829d24724e
ORCID for Brigitte Vollmer: ORCID iD orcid.org/0000-0003-4088-5336

Catalogue record

Date deposited: 27 May 2020 16:31
Last modified: 29 Jul 2020 04:02

Export record

Contributors

Author: Zhen Tang
Author: Sasan Mahmoodi
Author: Srinandan Dasmahapatra
Author: Angela Darker

University divisions

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×