In kumeS/rMiW: rMiW - R package for providing the analysis workflow for microscopy-based images

BiocStyle::markdown()

Last modified: r as.POSIXlt(file.info("rMiW_01_Basic.Rmd")$mtime, format="%Y-%m-%d %H:%M:%S", tz="Japan")
Compiled: r as.POSIXlt(x=Sys.time(), format="%Y-%m-%d %H:%M:%S", tz="Japan")

Getting started

#Install packages
install.packages(c("devtools", "BiocManager"), repos="http://cran.r-project.org")
BiocManager::install(c("EBImage", "BioImageDbs"), force = TRUE )
devtools::install_github( "kumeS/rMiW", force = TRUE )

#Load packages
library(EBImage)
#remove.packages("rMiW")
library(rMiW)

Optional: Update (ver 3.14)

BiocManager::install(version = "3.14")

Import a kidney image from rMiW

The displayed image (`Mouse01_Kid_x20_z0_RR01.png') is an image of whole slide imaging (WSI) for observing the kidney tissue of C57BL/6J mouse (male, 10 week-old) stained by H&E.

Read image

file <- system.file("extdata", "Mouse01_Kid_x20_z0_RR01.png", package="rMiW")
file

#Read image
Img <- EBImage::readImage(files = file)
str(Img)

Here, :: in R script indicates an explicit relation between the package and the functions.

Visualization

#Visualization
EBImage::display(Img, method = "raster")

##Skip execution for this chunk.
options(EBImage.display = "raster")
file <- system.file("extdata", "Mouse01_Kid_x20_z0_RR01.png", package="rMiW")
img <- EBImage::resize(EBImage::readImage(files = file), w = 500)
EBImage::display(img, method = "raster")

Basic image processing

In this section, we will handle the Image object of EBIimage and perform image processing.

Convert to the grey image

#Read image: delete the 4th element of 3th dimension
#Img <- EBImage::readImage(files = file)

#Convert to the gray image
GrayImg <- rMiW::toGrayScale(Img, mode = "luminance")
str(GrayImg)
str(Img)

#Visualization
EBImage::display(GrayImg, method = "raster")

##Skip execution for this chunk.
file <- system.file("extdata", "Mouse01_Kid_x20_z0_RR01.png", package="rMiW")
img <- rMiW::toGrayScale(EBImage::resize(EBImage::readImage(files = file), w = 500), mode = "luminance")
EBImage::display(img, method = "raster")

Resize the image

#1% area size
Img10 <- EBImage::resize(Img, 
                         w = round(dim(Img)[1]*0.1, 0), 
                         filter = "bilinear")

#6% area size
Img25 <- EBImage::resize(Img, 
                         w = round(dim(Img)[1]*0.25, 0), 
                         filter = "bilinear")

#25% area size
Img50 <- EBImage::resize(Img, 
                         w = round(dim(Img)[1]*0.5, 0), 
                         filter = "bilinear")

#50% area size
Img70 <- EBImage::resize(Img, 
                         w = round(dim(Img)[1]*0.707, 0), 
                         filter = "bilinear")

#Visualization
par(mfrow=c(2,2))
EBImage::display(Img10, method = "raster")
text(x = dim(Img10)[1]/20, y = dim(Img10)[2]/10, label = "1% area size", adj = c(0,0), col = "black", cex = 1.5)
EBImage::display(Img25, method = "raster")
text(x = dim(Img25)[1]/20, y = dim(Img25)[2]/10, label = "6% area size", adj = c(0,0), col = "black", cex = 1.5)
EBImage::display(Img50, method = "raster")
text(x = dim(Img50)[1]/20, y = dim(Img50)[2]/10, label = "25% area size", adj = c(0,0), col = "black", cex = 1.5)
EBImage::display(Img70, method = "raster")
text(x = dim(Img70)[1]/20, y = dim(Img70)[2]/10, label = "50% area size", adj = c(0,0), col = "black", cex = 1.5)

##Skip execution for this chunk.
#Read image
file <- system.file("extdata", "Mouse01_Kid_x20_z0_RR01.png", package="rMiW")
Img <- EBImage::readImage(files = file)

#1% area size
Img10 <- EBImage::resize(Img, w = round(dim(Img)[1]*0.1, 0), filter = "bilinear")
#6% area size
Img25 <- EBImage::resize(Img, w = round(dim(Img)[1]*0.25, 0), filter = "bilinear")
#25% area size
Img50 <- EBImage::resize(Img, w = round(dim(Img)[1]*0.5, 0), filter = "bilinear")
#50% area size
Img70 <- EBImage::resize(Img, w = round(dim(Img)[1]*0.707, 0), filter = "bilinear")

par(mfrow=c(2,2))
EBImage::display(Img10, method = "raster")
text(x = dim(Img10)[1]/20, y = dim(Img10)[2]/10, label = "1% area size", adj = c(0,0), col = "black", cex = 1.5)
EBImage::display(Img25, method = "raster")
text(x = dim(Img25)[1]/20, y = dim(Img25)[2]/10, label = "6% area size", adj = c(0,0), col = "black", cex = 1.5)
EBImage::display(Img50, method = "raster")
text(x = dim(Img50)[1]/20, y = dim(Img50)[2]/10, label = "25% area size", adj = c(0,0), col = "black", cex = 1.5)
EBImage::display(Img70, method = "raster")
text(x = dim(Img70)[1]/20, y = dim(Img70)[2]/10, label = "50% area size", adj = c(0,0), col = "black", cex = 1.5)

Transpose the image

Here we will perform some rotation operations.

#Transpose the image
ImgTrans <- EBImage::transpose(Img)

#Flip or flop the image
Imgflip <- EBImage::flip(Img)
Imgflop <- EBImage::flop(Img)

#Visualization
par(mfrow=c(2,2))
EBImage::display(Img, method = "raster")
text(x = dim(Img)[1]/20, y = dim(Img)[2]/10, label = "Original", adj = c(0,0), col = "black", cex = 1.5)
EBImage::display(ImgTrans, method = "raster")
text(x = dim(ImgTrans)[1]/20, y = dim(ImgTrans)[2]/10, label = "Transpose", adj = c(0,0), col = "black", cex = 1.5)
EBImage::display(Imgflip, method = "raster")
text(x = dim(Imgflip)[1]/20, y = dim(Imgflip)[2]/10, label = "Flip", adj = c(0,0), col = "black", cex = 1.5)
EBImage::display(Imgflop, method = "raster")
text(x = dim(Imgflop)[1]/20, y = dim(Imgflop)[2]/10, label = "Flop", adj = c(0,0), col = "black", cex = 1.5)

##Skip execution for this chunk.
#Read image
file <- system.file("extdata", "Mouse01_Kid_x20_z0_RR01.png", package="rMiW")
Img <- EBImage::readImage(files = file)

#Transpose the image
ImgTrans <- EBImage::transpose(Img)

#Flip or flop the image
Imgflip <- EBImage::flip(Img)
Imgflop <- EBImage::flop(Img)

#Visualization
par(mfrow=c(2,2))
EBImage::display(Img, method = "raster")
text(x = dim(Img)[1]/20, y = dim(Img)[2]/10, label = "Original", adj = c(0,0), col = "black", cex = 1.5)
EBImage::display(ImgTrans, method = "raster")
text(x = dim(ImgTrans)[1]/20, y = dim(ImgTrans)[2]/10, label = "Transpose", adj = c(0,0), col = "black", cex = 1.5)
EBImage::display(Imgflip, method = "raster")
text(x = dim(Imgflip)[1]/20, y = dim(Imgflip)[2]/10, label = "Flip", adj = c(0,0), col = "black", cex = 1.5)
EBImage::display(Imgflop, method = "raster")
text(x = dim(Imgflop)[1]/20, y = dim(Imgflop)[2]/10, label = "Flop", adj = c(0,0), col = "black", cex = 1.5)

• Transpose : 90 degree rotation + Horizontal rotation (90度回転 +左右方向の回転)
• Flip : Vertical rotation (上下方向の回転)
• Flop : Horizontal rotation (左右方向の回転)

Save image

We can save the file in image format (jpeg, png, tiff) or R binary (.Rds).

#Save as PNG
EBImage::writeImage(Img, files = "./Img.png",
                    type = "png")

#Save as TIFF
EBImage::writeImage(Img, files = "./Img.tif",
                    type = "tiff")

#Save as R binary for single Objects (.Rda)
saveRDS(Img, "./Img.Rds")
dir()

#Read Rds format
ImgRds <- readRDS("./Img.Rds")
str(ImgRds)

When we save the object/variable in R as a Rds file and load it, the same R object will be reproduced.

Mark objects in images

Overlap two images and color them.

file <- system.file("extdata", "Cell_Img.Rds", package="rMiW")

#Read image
CellImg <- readRDS(file)
str(CellImg)

#Cell image
par(mfrow=c(1,1))
EBImage::display(CellImg$X, method = "raster")

#Display them side-by-side.
EBImage::display(EBImage::combine(CellImg$X, CellImg$Y), 
                 nx=2, all=TRUE, spacing = 0.01, margin = 70, method = "raster")

#Overlap them
EBImage::display(EBImage::paintObjects(CellImg$Y,
                                       EBImage::toRGB(CellImg$X),
                                       opac=c(0.2, 0.2),
                                       col=c("red","red"), thick=TRUE, closed=FALSE),
                 method = "raster")

#rMiW function
rMiW::ImageView2D(ImgArray_x=CellImg$X,
                  ImgArray_y=CellImg$Y,
                  ImgN=1, 
                  lab=c("Original", "Overlay", "Ground truth"))

Use drop=FALSE to prevent the array from being deformed

file <- system.file("extdata", "Cell_Img.Rds", package="rMiW")
file

#Read image
CellImg <- readRDS(file)
str(CellImg)

#drop=TRUE
CellImgT <- CellImg$X[1:512,1:512,,drop=TRUE]
str(CellImgT)

#drop=FALSE
CellImgF <- CellImg$X[1:512,1:512,,drop=FALSE]
str(CellImgF)

Basic clustering using k-means

k-means clustering is an unsupervised clustering technique to partition N observations into k clusters in which each observation belongs to the cluster with the nearest mean.

The clustering with a compressed image of 1024x1024px

Here, we use the k-means clustering to divide the RGB intensity of the image into three classes.

#Read image: delete the 4th element of 3th dimension
#Load from the R binary
Img <- readRDS(system.file("extdata", "Mouse01_Kid_x20_z0_RR01.Rds", package="rMiW"))
str(Img)

#Resize 1024x1024 and perform 3 clustering 
ImgClus3 <- rMiW::Img2DClustering(x=Img, Cluster = 3, XY=1024)
str(ImgClus3)

#Visualize as a color image
rMiW::rasterMiW(ImgClus3, method = "raster")

#Barplot
Calc <- table(unlist(ImgClus3$Cluster)*ImgClus3$ClusterNumber)
barplot(Calc,
        ylab="Pixel number of cluster", ylim=c(0, max(Calc)*1.25), 
        col=colorspace::rainbow_hcl(ImgClus3$ClusterNumber, c = 70))

Session information {.unnumbered}

sessionInfo()

kumeS/rMiW documentation built on Dec. 21, 2021, 8:43 a.m.