3 research outputs found
Tensor based multichannel reconstruction for breast tumours identification from DCE-MRIs
A new methodology based on tensor algebra that uses a higher order singular value decomposition
to perform three-dimensional voxel reconstruction from a series of temporal images
obtained using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is proposed.
Principal component analysis (PCA) is used to robustly extract the spatial and temporal
image features and simultaneously de-noise the datasets. Tumour segmentation on
enhanced scaled (ES) images performed using a fuzzy C-means (FCM) cluster algorithm is
compared with that achieved using the proposed tensorial framework. The proposed algorithm
explores the correlations between spatial and temporal features in the tumours. The
multi-channel reconstruction enables improved breast tumour identification through
enhanced de-noising and improved intensity consistency. The reconstructed tumours have
clear and continuous boundaries; furthermore the reconstruction shows better voxel clustering
in tumour regions of interest. A more homogenous intensity distribution is also observed,
enabling improved image contrast between tumours and background, especially in places
where fatty tissue is imaged. The fidelity of reconstruction is further evaluated on the basis
of five new qualitative metrics. Results confirm the superiority of the tensorial approach. The
proposed reconstruction metrics should also find future applications in the assessment of
other reconstruction algorithms
Textural Kinetics: A Novel Dynamic Contrast-Enhanced (DCE)-MRI Feature for Breast Lesion Classification
Dynamic contrast-enhanced (DCE)-magnetic resonance imaging (MRI) of the breast has emerged as an adjunct imaging tool to conventional X-ray mammography due to its high detection sensitivity. Despite the increasing use of breast DCE-MRI, specificity in distinguishing malignant from benign breast lesions is low, and interobserver variability in lesion classification is high. The novel contribution of this paper is in the definition of a new DCE-MRI descriptor that we call textural kinetics, which attempts to capture spatiotemporal changes in breast lesion texture in order to distinguish malignant from benign lesions. We qualitatively and quantitatively demonstrated on 41 breast DCE-MRI studies that textural kinetic features outperform signal intensity kinetics and lesion morphology features in distinguishing benign from malignant lesions. A probabilistic boosting tree (PBT) classifier in conjunction with textural kinetic descriptors yielded an accuracy of 90%, sensitivity of 95%, specificity of 82%, and an area under the curve (AUC) of 0.92. Graph embedding, used for qualitative visualization of a low-dimensional representation of the data, showed the best separation between benign and malignant lesions when using textural kinetic features. The PBT classifier results and trends were also corroborated via a support vector machine classifier which showed that textural kinetic features outperformed the morphological, static texture, and signal intensity kinetics descriptors. When textural kinetic attributes were combined with morphologic descriptors, the resulting PBT classifier yielded 89% accuracy, 99% sensitivity, 76% specificity, and an AUC of 0.91