mri: Intensities of MRI images

In mrfDepth: Depth Measures in Multivariate, Regression and Functional Settings

Intensities of MRI images

Description

Felipe et al. (2005) obtained intensities of MRI images of 9 different parts of the human body (plus a group consisting of all remaining body regions, which was of course very heterogeneous). They then transformed their data to univariate curves.

Usage

data(mri)

Format

A list of arrays corresponding to each bodypart. For each bodypart, a three-dimensional t = 99 by n by p = 1 array is available. The index t corresponds to the different points of measurement, the index n to the different observations.

Details

When using this data set please cite both Felipe et al. (2005) and Hubert et al. (2017).

Source

Felipe J.C., Traina A.J.M., Traina C. (2005). Global warp metric distance: boosting content-based image retrieval through histograms. Proceedings of the Seventh IEEE International Symposium on Multimedia (ISM05), p.8.

Chen Y., Keogh E., Hu B., Begum N., Bagnall A., Mueen A., Batista G.J. (2015). The UCR Time Series Classification Archive. [http://www.cs.ucr.edu/~eamonn/time_series_data]

References

Hubert M., Rousseeuw P.J., Segaert P. (2017). Multivariate and functional classification using depth and distance. Advances in Data Analysis and Classification, 11, 445–466.

Examples

data(mri)
par(mfrow = c(2,1))
matplot(y = mri$bodypart1[,,1],
        type = "l", col = "black", lty = 1, 
        xlab = "", ylab="", main = "bodypart 1")
matplot(y = mri$bodypart2[,,1],
        type = "l", col = "black", lty = 1, 
        xlab = "", ylab="", main = "bodypart 2")
par(mfrow = c(1,1))

mrfDepth documentation built on Oct. 6, 2023, 5:07 p.m.