ArticleProceedings of SPIE--the International Society for Optical Engineering2014
Investigating the use of texture features for analysis of breast lesions on contrast-enhanced cone beam CT.
Article in Proceedings of SPIE--the International Society for Optical Engineering, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Radiomics in photon-counting dedicated breast CT: potential of texture analysis for breast density classification.European radiology experimental · 2022Article
- Dedicated breast CT: state of the art-Part II. Clinical application and future outlook.European radiology · 2022Review
- Relationship between computer segmentation performance and computer classification performance in breast CT: A simulation study using RGI segmentation and LDA classification.Medical physics · 2018Article
- Optimal reconstruction and quantitative image features for computer-aided diagnosis tools for breast CT.Medical physics · 2017Article
- Local curvature analysis for classifying breast tumors: Preliminary analysis in dedicated breast CT.Medical physics · 2015Article
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Authors and funding
6 authors.
Funding
Abstract
Cone beam computed tomography (CBCT) has found use in mammography for imaging the entire breast with sufficient spatial resolution at a radiation dose within the range of that of conventional mammography. Recently, enhancement of lesion tissue through the use of contrast agents has been proposed for cone beam CT. This study investigates whether the use of such contrast agents improves the ability of texture features to differentiate lesion texture from healthy tissue on CBCT in an automated manner. For this purpose, 9 lesions were annotated by an experienced radiologist on both regular and contrast-enhanced CBCT images using two-dimensional (2D) square ROIs. These lesions were then segmented, and each pixel within the lesion ROI was assigned a label - lesion or non-lesion, based on the segmentation mask. On both sets of CBCT images, four three-dimensional (3D) Minkowski Functionals were used to characterize the local topology at each pixel. The resulting feature vectors were then used in a machine learning task involving support vector regression with a linear kernel (SVRlin) to classify each pixel as belonging to the lesion or non-lesion region of the ROI. Classification performance was assessed using the area under the receiver-operating characteristic (ROC) curve (AUC). Minkowski Functionals derived from contrast-enhanced CBCT images were found to exhibit significantly better performance at distinguishing between lesion and non-lesion areas within the ROI when compared to those extracted from CBCT images without contrast enhancement (
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