1 Getting started
- 1.1 Optional: Update (ver 3.14)
2 Import a kidney image from rMiW
- 2.1 Read image
- 2.2 Visualization
3 Basic image processing
4 Basic clustering using k-means
- 4.1 The clustering with a compressed image of 1024x1024px
Session information

Last modified: 2021-11-03 20:03:15
Compiled: 2021-11-03 21:55:33

1 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)

1.1 Optional: Update (ver 3.14)

BiocManager::install(version = "3.14")

2 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.

2.1 Read image

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

## [1] "/Library/Frameworks/R.framework/Versions/4.1/Resources/library/rMiW/extdata/Mouse01_Kid_x20_z0_RR01.png"

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

## Formal class 'Image' [package "EBImage"] with 2 slots
##   ..@ .Data    : num [1:1257, 1:1038, 1:3] 0.902 0.906 0.906 0.902 0.906 ...
##   ..@ colormode: int 2
##   ..$ dim: int [1:3] 1257 1038 3

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

2.2 Visualization

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

Figure 1: Mouse01_Kid_x20_z0_RR01.png

3 Basic image processing

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

3.1 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")

Figure 2: The gray image of Mouse01_Kid_x20_z0_RR01.png

3.2 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)

Figure 3: The resized images of Mouse01_Kid_x20_z0_RR01.png

3.3 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)

Figure 4: The rotated images of Mouse01_Kid_x20_z0_RR01.png

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)

3.4 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()

##  [1] "Img.png"                       "Img.Rds"                      
##  [3] "Img.tif"                       "rMiW_00_GPU.html"             
##  [5] "rMiW_00_GPU.Rmd"               "rMiW_00_installation.html"    
##  [7] "rMiW_00_installation.Rmd"      "rMiW_01_Basic_eval_files"     
##  [9] "rMiW_01_Basic_eval.html"       "rMiW_01_Basic_eval.Rmd"       
## [11] "rMiW_01_Basic.html"            "rMiW_01_Basic.Rmd"            
## [13] "rMiW_02_BioImageDbs_eval.html" "rMiW_02_BioImageDbs_eval.Rmd" 
## [15] "rMiW_02_BioImageDbs.html"      "rMiW_02_BioImageDbs.Rmd"      
## [17] "rMiW_03_GAN.html"              "rMiW_03_GAN.Rmd"

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

## Formal class 'Image' [package "EBImage"] with 2 slots
##   ..@ .Data    : num [1:1257, 1:1038, 1:3] 0.902 0.906 0.906 0.902 0.906 ...
##   ..@ colormode: int 2
##   ..$ dim: int [1:3] 1257 1038 3

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

3.5 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)

## List of 2
##  $ X:Formal class 'Image' [package "EBImage"] with 2 slots
##   .. ..@ .Data    : num [1:512, 1:512, 1] 0.518 0.502 0.502 0.522 0.506 ...
##   .. ..@ colormode: int 0
##   .. ..$ dim: int [1:3] 512 512 1
##  $ Y:Formal class 'Image' [package "EBImage"] with 2 slots
##   .. ..@ .Data    : num [1:512, 1:512, 1] 0 0 0 0 0 0 0 0 0 0 ...
##   .. ..@ colormode: int 0
##   .. ..$ dim: int [1:3] 512 512 1

#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"))

3.6 Use `drop=FALSE` to prevent the array from being deformed

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

## [1] "/Library/Frameworks/R.framework/Versions/4.1/Resources/library/rMiW/extdata/Cell_Img.Rds"

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

## List of 2
##  $ X:Formal class 'Image' [package "EBImage"] with 2 slots
##   .. ..@ .Data    : num [1:512, 1:512, 1] 0.518 0.502 0.502 0.522 0.506 ...
##   .. ..@ colormode: int 0
##   .. ..$ dim: int [1:3] 512 512 1
##  $ Y:Formal class 'Image' [package "EBImage"] with 2 slots
##   .. ..@ .Data    : num [1:512, 1:512, 1] 0 0 0 0 0 0 0 0 0 0 ...
##   .. ..@ colormode: int 0
##   .. ..$ dim: int [1:3] 512 512 1

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

## Formal class 'Image' [package "EBImage"] with 2 slots
##   ..@ .Data    : num [1:512, 1:512] 0.518 0.502 0.502 0.522 0.506 ...
##   ..@ colormode: int 0
##   ..$ dim: int [1:2] 512 512

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

## Formal class 'Image' [package "EBImage"] with 2 slots
##   ..@ .Data    : num [1:512, 1:512, 1] 0.518 0.502 0.502 0.522 0.506 ...
##   ..@ colormode: int 0
##   ..$ dim: int [1:3] 512 512 1

4 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.

4.1 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)

## Formal class 'Image' [package "EBImage"] with 2 slots
##   ..@ .Data    : num [1:1257, 1:1038, 1:3] 0.902 0.906 0.906 0.902 0.906 ...
##   ..@ colormode: int 2
##   ..$ dim: int [1:3] 1257 1038 3

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

## List of 3
##  $ Original     : num [1:1257, 1:1038, 1:3] 0.902 0.906 0.906 0.902 0.906 ...
##  $ Cluster      : num [1:1257, 1:1038] 0.333 0.333 0.333 0.333 0.333 ...
##  $ ClusterNumber: num 3

#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

## R version 4.1.1 (2021-08-10)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Catalina 10.15.7
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] ja_JP.UTF-8/ja_JP.UTF-8/ja_JP.UTF-8/C/ja_JP.UTF-8/ja_JP.UTF-8
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] rMiW_0.99.4      EBImage_4.36.0   BiocStyle_2.22.0
## 
## loaded via a namespace (and not attached):
##  [1] Rcpp_1.0.7          locfit_1.5-9.4      lattice_0.20-45    
##  [4] fftwtools_0.9-11    png_0.1-7           visNetwork_2.1.0   
##  [7] assertthat_0.2.1    zeallot_0.1.0       digest_0.6.28      
## [10] utf8_1.2.2          R6_2.5.1            tiff_0.1-8         
## [13] evaluate_0.14       highr_0.9           pillar_1.6.4       
## [16] tfruns_1.5.0        rlang_0.4.12        whisker_0.4        
## [19] jquerylib_0.1.4     magick_2.7.3        Matrix_1.3-4       
## [22] reticulate_1.22     rmarkdown_2.11      DiagrammeR_1.0.6.1 
## [25] keras_2.6.1         stringr_1.4.0       htmlwidgets_1.5.4  
## [28] igraph_1.2.7        RCurl_1.98-1.5      compiler_4.1.1     
## [31] xfun_0.27           pkgconfig_2.0.3     BiocGenerics_0.40.0
## [34] base64enc_0.1-3     tensorflow_2.6.0    htmltools_0.5.2    
## [37] tidyselect_1.1.1    tibble_3.1.5        bookdown_0.24      
## [40] mmand_1.6.1         fansi_0.5.0         crayon_1.4.2       
## [43] dplyr_1.0.7         bitops_1.0-7        grid_4.1.1         
## [46] jsonlite_1.7.2      lifecycle_1.0.1     DBI_1.1.1          
## [49] magrittr_2.0.1      stringi_1.7.5       bslib_0.3.1        
## [52] ellipsis_0.3.2      vctrs_0.3.8         generics_0.1.1     
## [55] RColorBrewer_1.1-2  tools_4.1.1         glue_1.4.2         
## [58] purrr_0.3.4         jpeg_0.1-9          abind_1.4-5        
## [61] fastmap_1.1.0       yaml_2.2.1          colorspace_2.0-2   
## [64] BiocManager_1.30.16 knitr_1.36          sass_0.4.0

rMiW: 01. Providing a basic workflow for microscopy-based images

2021-11-03

Package

Contents

1 Getting started

1.1 Optional: Update (ver 3.14)

2 Import a kidney image from rMiW

2.1 Read image

2.2 Visualization

3 Basic image processing

3.1 Convert to the grey image

3.2 Resize the image

3.3 Transpose the image

3.4 Save image

3.5 Mark objects in images

3.6 Use `drop=FALSE` to prevent the array from being deformed

4 Basic clustering using k-means

4.1 The clustering with a compressed image of 1024x1024px

Session information

rMiW: 01. Providing a basic workflow for microscopy-based images

2021-11-03

Package

Contents

1 Getting started

1.1 Optional: Update (ver 3.14)

2 Import a kidney image from rMiW

2.1 Read image

2.2 Visualization

3 Basic image processing

3.1 Convert to the grey image

3.2 Resize the image

3.3 Transpose the image

3.4 Save image

3.5 Mark objects in images

3.6 Use drop=FALSE to prevent the array from being deformed

4 Basic clustering using k-means

4.1 The clustering with a compressed image of 1024x1024px

Session information

3.6 Use `drop=FALSE` to prevent the array from being deformed