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

BMC Medical Imaging

Research Article

Philip Evans¹  David Windridge¹  Emma Harris²  Prabhjot Juneja³ 

[1] Centre for Vision Speech and Signal Processing, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford, UK;Joint Department of Physics, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, UK;North Sydney Cancer Center, Royal North Shore Hospital, Sydney, Australia;Institute of Medical Physics, School of Physics, University of Sydney, Sydney, Australia;Joint Department of Physics, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, UK;

关键词: Breast radiotherapy;    Tissue segmentation;    Fibroglandular tissue distribution;   

DOI  :  10.1186/s12880-016-0107-2

 received in 2014-12-23, accepted in 2016-01-05,  发布年份 2016

来源: Springer

【 摘 要 】

BackgroundAccurate 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.MethodsPlanning 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.ResultsExperts 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.ConclusionThis 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 %.

【 授权许可】

CC BY   
© Juneja et al. 2016