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Stained Brain Slice
Example microscopic image of stained brain tissue slide.
Isolating Blue and Brown Regions
• i <- Brick(“Sample_1.tif”)
• names(i) <- c(“r”, “g”, “b”)
• Thresholding values:Blue <- (i$r < 125 & i$g < 150 & i$b > 10)*1 Brown <- (i$r > 0 & i$g > 0 & i$b < 165)*2
Blue Regions
Extracted Blue Regions Original Image
Brown Regions
Original ImageExtracted Brown Regions
Isolating Blue and Brown Patches
• i <- Brick(j)
• names(i) <- c(“r”, “g”, “b”)
• Thresholding values:Blue <- (i$r < 170 & i$g < 140 & i$b > 150)*1 Brown <- (i$r > 0 & i$g > 0 & i$b < 180)*2
• Combining two Raster Layers:Blue <- as.matrix(Blue)
Brown <- as.matrix(Brown)
Combined <- Blue+Brown Combined[Combined==1] <- 0 Combined[Combined==2] <- 0 Combined[Combined==3] <- 1 Combined <- raster(Combined)
Blue and Brown Regions
Extracted Blue Regions Extracted Blue + Brown Regions
Minimum Size Filter
Pixel <- 2500
Patches <- clump(Combined, directions = 8)
F <- as.data.frame(freq(Patches)) ExcludeID <- F$Value[which(F$Count < Pixel)]
PixelsRemoved<- Combined
PixelsRemoved[Combined %in% ExcludeID] <- NA
Value Count
1 1 1227
2 2 1
3 3 1
4 4 219
5 5 1
6 6 47
7 7 7275
Sample output of freq()
Size Filtered Image
Filtered Image Extracted Blue + Brown Regions
Output Image
Patch_Stats <- PatchStat(PixelsRemoved)
Total_Patches <- nrow(Patch_Stats)
extent(PixelsRemoved) <- extent(i)
plotRGB(i)
plot(PixelsRemoved, col="red", add=TRUE, legend=FALSE)
Example image output. Red regions represent counted patches
Batch ModeImage_Names <- as.data.frame(list.files())dir.create("ANALYSED_IMAGES")wd <- getwd()Output <- data.frame(matrix(nrow=0, ncol=2))
For (j in Image_Names[,1]){i <- brick(j)…Output [nrow(Output) + 1,] <- c(j , Total_Patches) Path <- file.path(wd,"ANALYSED_IMAGES", paste(j, ".pdf", sep=""))
pdf(Path) plotRGB(j)
plot(PixelsRemoved, col="red", add=TRUE, legend=FALSE) dev.off()}
Data_Path <- file.path(wd,"ANALYSED_IMAGES", paste("ANALYSED_DATA.txt", sep=""))write.table(Output, Data_Path, sep="\t")
Measuring Oak Vessel Area
Example scanned oak tree core, with infilled vessels and coloured surface.
Unaltered oak tree core surface.
Vessels filled with chalk.
Vessels filled with chalk and core surface coloured.
Identifying Vessels
Identified vessels (right; green) from a prepared oak core sample (left).
Identifying Ring Boundariesi <- brick("realvessels.tif")dflist <- list()plotRGB(i)
repeat {
Year<- readline(prompt = "What Year? ") coordinates <- data.frame(locator(, type = "o", col = "red")) dflist[[Year]] <- coordinates if (Year == "FINISH"){ break }}
User defined annual ring boundaries (red)
Sorting Measured VesselsPoly_list <- list()Vessel_year_List <- list()
for (j in 1:length(dflist)){if (j == length(dflist)) {
break} if (j < length(dflist)) { U_boundary <- as.data.frame(dflist[j]) L_boundary <- as.data.frame(dflist[(j+1)]) L_boundary <- L_boundary[order(-L_boundary[,1]),] C_boundary <- rbind(U_boundary, L_boundary) Combined <- rbind(C_boundary,U_boundary[1,]) Poly_list[[names(dflist[j])]] <- SpatialPolygons(list(Polygons(list(Polygon(Combined)), j))) Vessel_year_List[[names(dflist[j])]] <- list(extract(("realvessels.tif", poly_list[[j]], unique)) }}
Upper boundary
Lower boundary
1. 2.
3.4.
Plotting Vessel ID’s
Vessel_Num <- (i)
Clump_ID <- getValues(Vessel_Num)
xy <- xyFromCell(Vessel_Num, 1:ncell(Vessel_Num))
Vessel_ID <- data.frame(xy, Clump_ID, is_clump = Vessel_Num [] %in% freq(Vessel_Num, useNA = 'no')[,1])
Vessel_Coordinates <- sapply(split(Vessel_ID[, c("x", "y")], Vessel_ID$Clump_ID), colMeans)
text(Vessel_Coordinates, labels = row.names(Patch_Stats), col="red")
Output
Example data output
Example output image
References
Thresholding and counting images:http://stackoverflow.com/questions/32074992/using-multi-spec-and-r-for-batch-image-analysis
Size filter:http://gis.stackexchange.com/questions/130993/remove-clumps-of-pixels-in-r
Numbering cells:http://stackoverflow.com/questions/15632630/get-coordinates-of-a-patch-in-a-raster-map-raster-package-in-r
