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Classification of fibroglandular tissue distribution in the breast based on radiotherapy planning CT.

Juneja, P, Evans, P, Windridge, D and Harris, E (2016) Classification of fibroglandular tissue distribution in the breast based on radiotherapy planning CT. BMC Med Imaging, 16 (1).

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Abstract

BACKGROUND: Accurate segmentation of breast tissues is required for a number of applications such as model based deformable registration in breast radiotherapy. The accuracy of breast tissue segmentation is affected by the spatial distribution (or pattern) of fibroglandular tissue (FT). The goal of this study was to develop and evaluate texture features, determined from planning computed tomography (CT) data, to classify the spatial distribution of FT in the breast. METHODS: Planning CT data of 23 patients were evaluated in this study. Texture features were derived from the radial glandular fraction (RGF), which described the distribution of FT within three breast regions (posterior, middle, and anterior). Using visual assessment, experts grouped patients according to FT spatial distribution: sparse or non-sparse. Differences in the features between the two groups were investigated using the Wilcoxon rank test. Classification performance of the features was evaluated for a range of support vector machine (SVM) classifiers. RESULTS: Experts found eight patients and 15 patients had sparse and non-sparse spatial distribution of FT, respectively. A large proportion of features (>9 of 13) from the individual breast regions had significant differences (p <0.05) between the sparse and non-sparse group. The features from middle region had most significant differences and gave the highest classification accuracy for all the SVM kernels investigated. Overall, the features from middle breast region achieved highest accuracy (91 %) with the linear SVM kernel. CONCLUSION: This study found that features based on radial glandular fraction provide a means for discriminating between fibroglandular tissue distributions and could achieve a classification accuracy of 91 %.

Item Type: Article
Subjects : CVSSP
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Vision Speech and Signal Processing
Authors :
AuthorsEmailORCID
Juneja, PUNSPECIFIEDUNSPECIFIED
Evans, PUNSPECIFIEDUNSPECIFIED
Windridge, DUNSPECIFIEDUNSPECIFIED
Harris, EUNSPECIFIEDUNSPECIFIED
Date : 2016
Identification Number : 10.1186/s12880-016-0107-2
Related URLs :
Additional Information : This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://​creativecommons.​org/​licenses/​by/​4.​0/​) The published version is available here: http://dx.doi.org/10.1186/s12880-016-0107-2
Depositing User : Symplectic Elements
Date Deposited : 29 Jan 2016 11:31
Last Modified : 09 Feb 2016 09:29
URI: http://epubs.surrey.ac.uk/id/eprint/809811

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