MRIaggr-calcFilter: Image filtration
In bozenne/MRIaggr: Management, Display, and Processing of Medical Imaging Data
Description
Usage
Arguments
Details
Value
See Also
Examples
Description
Apply a filter to an image.
Usage
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## S4 method for signature 'array'
calcFilter(object, filter, norm.filter = TRUE, bilateral = FALSE, na.rm = FALSE)
## S4 method for signature 'MRIaggr'
calcFilter(object, param, filter, norm.filter = TRUE, bilateral = FALSE,
na.rm = FALSE, name_newparam = NULL,
verbose = optionsMRIaggr("verbose"), update.object = FALSE, overwrite = FALSE)
Arguments
object
an array or an object of class MRIaggr. REQUIRED.
param
the contrast parameter to be filtered. character vector. REQUIRED.
filter
the filter to use. Can be a matrix or an array, or a name indicating which filter should be used. REQUIRED.
norm.filter
should the filtered correspond to a weighted mean over site ? (or a weighted sum). logical.
bilateral
should the influence of each neighbor be ponderated by the difference in signal with the considered observation ? logical.
na.rm
should observations with missing values in their neighbourhood be set to NA ? Otherwise the ponderation is adjusted. logical.
name_newparam
the name of the new parameters. character vector.
verbose
should the execution of the function be traced ? logical.
update.object
should the resulting filtered parameters be stored in object ? logical.
overwrite
if contrast parameters with the same names are already stored in object@data, can they be overwritten ? logical.
Details
ARGUMENTS:
Several types of pre-stored filters are availables and can be called by their name:
filter for smoothing purpose (e.g. "3D_G5") : see the details section of initFilter.
filter for neighbourhood definition purpose (e.g. "3D_N10") : see the details section of initNeighborhood.
norm.filter should be set to TRUE for gaussian, median or sobel filters to correct edge effects.
This lead to weight the neighboring value in order to offset the incomplete neighbourhood.
Value
An list containing :
[[res]] : a data.frame containing the coordinates and the filtered parameters.
[[filter]] : the name of the filter that has been used. character.
See Also
selectContrast to select the filtered parameter(s).
initFilter or codeinitNeighborhood to select pre-stored filters.
Examples
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#### 1- array method ####
## load a NIFTI file
path.Pat1 <- system.file(file.path("nifti"), package = "MRIaggr")
nifti.Pat1_DWI_t0 <- readMRI(file.path(path.Pat1, "DWI_t0"), format = "nifti")
## before filtering
graphics::image(nifti.Pat1_DWI_t0[,,1,1])
## after median filtering
niftiF.Pat1_DWI_t0 <- calcFilter(nifti.Pat1_DWI_t0[,,,1], filter = "2D_M3")$res
graphics::image(niftiF.Pat1_DWI_t0[,,1])
#### 2- MRIaggr method ####
## load a MRIaggr object
data("MRIaggr.Pat1_red", package = "MRIaggr")
## compute and allocate filtered parameter to the MRIaggr object
# gaussian filter
calcFilter(MRIaggr.Pat1_red, param = c("T2_FLAIR_t2","DWI_t0","TTP_t0"),
filter = "2D_G3", bilateral = FALSE, na.rm = FALSE,
update.object = TRUE, overwrite = TRUE)
selectParameter(MRIaggr.Pat1_red)
# median filter
calcFilter(MRIaggr.Pat1_red, param = c("T2_FLAIR_t2","DWI_t0","TTP_t0"),
filter = "2D_M3", na.rm = FALSE, update.object = TRUE, overwrite = TRUE)
## display
par(mfrow = c(2,2), mar = c(2,2,2,2))
multiplot(MRIaggr.Pat1_red, param = "T2_FLAIR_t2",
num.main = FALSE, main = "raw",
num = 1, window = NULL, breaks = c(-100,seq(0, 450),601))
multiplot(MRIaggr.Pat1_red, param = "T2_FLAIR_t2_2D_G3",
num.main = FALSE, main = "2D_G3",
num = 1, legend = FALSE, window = NULL, breaks = c(-100,seq(0,450),601))
multiplot(MRIaggr.Pat1_red, param = "T2_FLAIR_t2_2D_M3",
num.main = FALSE, main = "2D_M3",
num = 1, legend = FALSE, window = NULL, breaks = c(-100,seq(0, 450),601))
## see the results of the different filters
# G : Gaussian filter
resG <- calcFilter(MRIaggr.Pat1_red, param = "T2_FLAIR_t2",
filter = "2D_G3")
# M : median filter
resM <- calcFilter(MRIaggr.Pat1_red, param = "T2_FLAIR_t2",
filter = "2D_M3")
# S : Sobel filter
resS <- calcFilter(MRIaggr.Pat1_red, param = "T2_FLAIR_t2",
filter = "2D_Sx")
# I
resI.T <- calcFilter(MRIaggr.Pat1_red, param = "MASK_T2_FLAIR_t2",
filter = "2D_I3", norm.filter = TRUE)
resI.F <- calcFilter(MRIaggr.Pat1_red, param = "MASK_T2_FLAIR_t2",
filter = "2D_I3", norm.filter = FALSE)
# N
resN.T <- calcFilter(MRIaggr.Pat1_red, param = "MASK_T2_FLAIR_t2",
filter = "3D_N10", norm.filter = TRUE)
resN.F <- calcFilter(MRIaggr.Pat1_red, param = "MASK_T2_FLAIR_t2",
filter = "3D_N10", norm.filter = FALSE)
## display
par(mfrow = c(2,2), mar = rep(2,4), mgp = c(2,0.75,0))
breaks <- seq(-50,500,1)
multiplot(MRIaggr.Pat1_red, param = "T2_FLAIR_t2", num = 3,
breaks = breaks, window = NULL, legend = FALSE,
main = "no filtering", num.main = FALSE, main.legend = "")
multiplot(resS$res[,c("i","j","k")], contrast = resS$res[,4],
num = 1, window = NULL, legend = FALSE,
palette = "cm.colors", breaks = seq(-100, 100),
main = "sobelX filtering", num.main = FALSE)
multiplot(resG$res[,c("i","j","k")], contrast = resG$res[,4],
num = 3, window = NULL, legend = FALSE, breaks = breaks,
main = "gaussian filtering", num.main = FALSE)
multiplot(resM$res[,c("i","j","k")], contrast = resM$res[,4],
num = 3, window = NULL, legend = FALSE, breaks = breaks,
main = "median filtering", num.main = FALSE)
layout(matrix(1:6, nrow = 3, ncol = 2, byrow = TRUE), widths = c(2,1), heights = rep(1,3))
par(mar = rep(2,4), mgp = c(2,0.75,0))
multiplot(MRIaggr.Pat1_red, param = "MASK_T2_FLAIR_t2", num = 3,
window = NULL, legend = TRUE, main = "raw", num.main = FALSE, main.legend = "")
multiplot(resI.T$res[,c("i","j","k")], contrast = resI.T$res[,4],
num = 3, window = NULL, legend = TRUE,
main = "Influence filtering - norm = TRUE", num.main = FALSE)
multiplot(resI.F$res[,c("i","j","k")], contrast = resI.F$res[,4],
num = 3, window = NULL, legend = TRUE,
main = "Influence filtering - norm = FALSE", num.main = FALSE)
layout(matrix(1:6, nrow = 3, ncol = 2, byrow = TRUE), widths = c(2,1), heights = rep(1,3))
par(mar = rep(2,4), mgp = c(2,0.75,0))
multiplot(MRIaggr.Pat1_red, param = "MASK_T2_FLAIR_t2", num = 3,
window = NULL, legend = TRUE, main = "raw", num.main = FALSE, main.legend = "")
multiplot(resN.T$res[,c("i","j","k")], contrast = resN.T$res[,4],
num = 3, window = NULL, legend = TRUE,
main = "Neighborhood3D filtering - norm = TRUE", num.main = FALSE)
multiplot(resN.F$res[,c("i","j","k")], contrast = resN.F$res[,4],
num = 3, window = NULL, legend = TRUE,
main = "Neighborhood3D filtering - norm = FALSE", num.main = FALSE)
bozenne/MRIaggr documentation built on May 13, 2019, 1:39 a.m.
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Description Usage Arguments Details Value See Also Examples
Description
Apply a filter to an image.
Usage
1 2 3 4 5 6 7 | ## S4 method for signature 'array'
calcFilter(object, filter, norm.filter = TRUE, bilateral = FALSE, na.rm = FALSE)
## S4 method for signature 'MRIaggr'
calcFilter(object, param, filter, norm.filter = TRUE, bilateral = FALSE,
na.rm = FALSE, name_newparam = NULL,
verbose = optionsMRIaggr("verbose"), update.object = FALSE, overwrite = FALSE)
|
Arguments
object |
an |
param |
the contrast parameter to be filtered. character vector. REQUIRED. |
filter |
the filter to use. Can be a matrix or an array, or a name indicating which filter should be used. REQUIRED. |
norm.filter |
should the filtered correspond to a weighted mean over site ? (or a weighted sum). logical. |
bilateral |
should the influence of each neighbor be ponderated by the difference in signal with the considered observation ? logical. |
na.rm |
should observations with missing values in their neighbourhood be set to NA ? Otherwise the ponderation is adjusted. logical. |
name_newparam |
the name of the new parameters. character vector. |
verbose |
should the execution of the function be traced ? logical. |
update.object |
should the resulting filtered parameters be stored in |
overwrite |
if contrast parameters with the same names are already stored in |
Details
ARGUMENTS:
Several types of pre-stored filters are availables and can be called by their name:
filter for smoothing purpose (e.g. "3D_G5") : see the details section of
initFilter.filter for neighbourhood definition purpose (e.g. "3D_N10") : see the details section of
initNeighborhood.
norm.filter should be set to TRUE for gaussian, median or sobel filters to correct edge effects.
This lead to weight the neighboring value in order to offset the incomplete neighbourhood.
Value
An list containing :
[[res]]: a data.frame containing the coordinates and the filtered parameters.[[filter]]: the name of the filter that has been used. character.
See Also
selectContrast to select the filtered parameter(s).
initFilter or codeinitNeighborhood to select pre-stored filters.
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 | #### 1- array method ####
## load a NIFTI file
path.Pat1 <- system.file(file.path("nifti"), package = "MRIaggr")
nifti.Pat1_DWI_t0 <- readMRI(file.path(path.Pat1, "DWI_t0"), format = "nifti")
## before filtering
graphics::image(nifti.Pat1_DWI_t0[,,1,1])
## after median filtering
niftiF.Pat1_DWI_t0 <- calcFilter(nifti.Pat1_DWI_t0[,,,1], filter = "2D_M3")$res
graphics::image(niftiF.Pat1_DWI_t0[,,1])
#### 2- MRIaggr method ####
## load a MRIaggr object
data("MRIaggr.Pat1_red", package = "MRIaggr")
## compute and allocate filtered parameter to the MRIaggr object
# gaussian filter
calcFilter(MRIaggr.Pat1_red, param = c("T2_FLAIR_t2","DWI_t0","TTP_t0"),
filter = "2D_G3", bilateral = FALSE, na.rm = FALSE,
update.object = TRUE, overwrite = TRUE)
selectParameter(MRIaggr.Pat1_red)
# median filter
calcFilter(MRIaggr.Pat1_red, param = c("T2_FLAIR_t2","DWI_t0","TTP_t0"),
filter = "2D_M3", na.rm = FALSE, update.object = TRUE, overwrite = TRUE)
## display
par(mfrow = c(2,2), mar = c(2,2,2,2))
multiplot(MRIaggr.Pat1_red, param = "T2_FLAIR_t2",
num.main = FALSE, main = "raw",
num = 1, window = NULL, breaks = c(-100,seq(0, 450),601))
multiplot(MRIaggr.Pat1_red, param = "T2_FLAIR_t2_2D_G3",
num.main = FALSE, main = "2D_G3",
num = 1, legend = FALSE, window = NULL, breaks = c(-100,seq(0,450),601))
multiplot(MRIaggr.Pat1_red, param = "T2_FLAIR_t2_2D_M3",
num.main = FALSE, main = "2D_M3",
num = 1, legend = FALSE, window = NULL, breaks = c(-100,seq(0, 450),601))
## see the results of the different filters
# G : Gaussian filter
resG <- calcFilter(MRIaggr.Pat1_red, param = "T2_FLAIR_t2",
filter = "2D_G3")
# M : median filter
resM <- calcFilter(MRIaggr.Pat1_red, param = "T2_FLAIR_t2",
filter = "2D_M3")
# S : Sobel filter
resS <- calcFilter(MRIaggr.Pat1_red, param = "T2_FLAIR_t2",
filter = "2D_Sx")
# I
resI.T <- calcFilter(MRIaggr.Pat1_red, param = "MASK_T2_FLAIR_t2",
filter = "2D_I3", norm.filter = TRUE)
resI.F <- calcFilter(MRIaggr.Pat1_red, param = "MASK_T2_FLAIR_t2",
filter = "2D_I3", norm.filter = FALSE)
# N
resN.T <- calcFilter(MRIaggr.Pat1_red, param = "MASK_T2_FLAIR_t2",
filter = "3D_N10", norm.filter = TRUE)
resN.F <- calcFilter(MRIaggr.Pat1_red, param = "MASK_T2_FLAIR_t2",
filter = "3D_N10", norm.filter = FALSE)
## display
par(mfrow = c(2,2), mar = rep(2,4), mgp = c(2,0.75,0))
breaks <- seq(-50,500,1)
multiplot(MRIaggr.Pat1_red, param = "T2_FLAIR_t2", num = 3,
breaks = breaks, window = NULL, legend = FALSE,
main = "no filtering", num.main = FALSE, main.legend = "")
multiplot(resS$res[,c("i","j","k")], contrast = resS$res[,4],
num = 1, window = NULL, legend = FALSE,
palette = "cm.colors", breaks = seq(-100, 100),
main = "sobelX filtering", num.main = FALSE)
multiplot(resG$res[,c("i","j","k")], contrast = resG$res[,4],
num = 3, window = NULL, legend = FALSE, breaks = breaks,
main = "gaussian filtering", num.main = FALSE)
multiplot(resM$res[,c("i","j","k")], contrast = resM$res[,4],
num = 3, window = NULL, legend = FALSE, breaks = breaks,
main = "median filtering", num.main = FALSE)
layout(matrix(1:6, nrow = 3, ncol = 2, byrow = TRUE), widths = c(2,1), heights = rep(1,3))
par(mar = rep(2,4), mgp = c(2,0.75,0))
multiplot(MRIaggr.Pat1_red, param = "MASK_T2_FLAIR_t2", num = 3,
window = NULL, legend = TRUE, main = "raw", num.main = FALSE, main.legend = "")
multiplot(resI.T$res[,c("i","j","k")], contrast = resI.T$res[,4],
num = 3, window = NULL, legend = TRUE,
main = "Influence filtering - norm = TRUE", num.main = FALSE)
multiplot(resI.F$res[,c("i","j","k")], contrast = resI.F$res[,4],
num = 3, window = NULL, legend = TRUE,
main = "Influence filtering - norm = FALSE", num.main = FALSE)
layout(matrix(1:6, nrow = 3, ncol = 2, byrow = TRUE), widths = c(2,1), heights = rep(1,3))
par(mar = rep(2,4), mgp = c(2,0.75,0))
multiplot(MRIaggr.Pat1_red, param = "MASK_T2_FLAIR_t2", num = 3,
window = NULL, legend = TRUE, main = "raw", num.main = FALSE, main.legend = "")
multiplot(resN.T$res[,c("i","j","k")], contrast = resN.T$res[,4],
num = 3, window = NULL, legend = TRUE,
main = "Neighborhood3D filtering - norm = TRUE", num.main = FALSE)
multiplot(resN.F$res[,c("i","j","k")], contrast = resN.F$res[,4],
num = 3, window = NULL, legend = TRUE,
main = "Neighborhood3D filtering - norm = FALSE", num.main = FALSE)
|
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