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The efficacy of the proposed algorithm is also tested for the experimental phantom data and in vivo breast data.
The accurate performance of this algorithm on MR data makes it a promising technique to apply on longitudinally acquired breast data available from other modalities.
The proposed method is tested on seven real dynamic time series consisting of 2 breast data sets and 5 abdomen data sets spanning 1196 images in all.
Here, we propose a method whereby serially acquired FDG-PET breast data sets can be spatially co-registered to enable the spatial comparison of parameter maps at the voxel level.
This paper describes the development and initial testing of an automated ultrasound imaging technique to acquire quantitative volumetric breast data; the clinical application being breast cancer diagnosis and management.
Average improvements in Universal Image Quality Index and Structural Similarity Index Metric of up to 28% and 24% on breast data and about 17%and9%9% on abdomen data, respectively, are obtained for the proposed method as against the baseline TV reconstruction with variable density random sampling pattern.
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We also applied GSEA to both breast cancer data and ET data.
The NTI is successfully applied on two breast cancer data sets to compute correlations between microarray data and clinical data.
All TCGA RNASeqV2 breast cancer data (*rsem.genes.results) were downloaded from TCGA data portal [ 24].
Breast cancer data from 1975 to 2005 collected by the GCR was considered representative of the Catalan breast cancer data.
The breast cancer data set from Uppsala and Stockholm cohorts with relevant clinical information have been described previously [32], [33].
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