add example usage to documentation. Single functions for auto_crop and...

add example usage to documentation. Single functions for auto_crop and measure_distance.  Passes BiocCheck with 0 errors, 0 warnings and 5 notes.

DESCRIPTION

@@ -15,13 +15,13 @@ Authors@R:
 Description: Synapsis is a Bioconductor software package for automated (unbiased and reproducible) analysis of meiotic immunofluorescence datasets. The primary functions of the software can i) identify cells in meiotic prophase that are labelled by a synaptonemal complex axis or central element protein, ii) isolate individual synaptonemal complexes and measure their physical length, iii) quantify foci and co-localise them with synaptonemal complexes, iv) measure interference between synaptonemal complex-associated foci. The software has applications that extend to multiple species and to the analysis of other proteins that label meiotic prophase chromosomes. The software converts meiotic immunofluorescence images into R data frames that are compatible with machine learning methods. Given a set of microscopy images of meiotic spread slides, synapsis crops images around individual single cells, counts colocalising foci on strands on a per cell basis, and measures the distance between foci on any given strand.
 biocViews:
   Software,
-  Visualization
+  Visualization,
+  SingleCell
 Depends: 
-    R (>= 4.0)
+    R (>= 4.1)
 Imports: EBImage, stats, utils, graphics
 License: MIT + file LICENSE
 Encoding: UTF-8
-LazyData: true
 RoxygenNote: 7.1.1
 VignetteBuilder: knitr
 Suggests: 

NAMESPACE

 # Generated by roxygen2: do not edit by hand
 
-export(auto_crop)
 export(auto_crop_fast)
 export(count_foci)
-export(crop_single_object)
-export(crop_single_object_fast)
-export(get_blobs)
 export(get_pachytene)
-export(keep_cells)
-export(measure_distances)
 export(measure_distances_general)
 importFrom(EBImage,bwlabel)
 importFrom(EBImage,channel)

R/auto_crop.R

-#' auto_crop
-#'
-#' crop an image around each viable cell candidate.
-#' @importFrom stats median sd
-#' @importFrom EBImage bwlabel channel colorLabels computeFeatures computeFeatures.basic computeFeatures.moment computeFeatures.shape display filter2 makeBrush readImage rgbImage rmObjects rotate writeImage
-#' @importFrom graphics text
-#' @importFrom utils str
-#' @export auto_crop
-#' @param img_path, path containing image data to analyse
-#' @param max_cell_area, Maximum pixel area of a cell candidate
-#' @param min_cell_area, Minimum pixel area of a cell candidate
-#' @param mean_pix, Mean pixel intensity of cell crop (in SYCP3 channel) for normalisation
-#' @param annotation, Choice to output pipeline choices (recommended to knit)
-#' @param blob_factor, Contrast factor to multiply original image by before smoothing/smudging
-#' @param bg_blob_factor, Contrast factor to multiply original image by to take background. Used prior to thresholding.
-#' @param offset, Pixel value offset from bg_blob_factor. Used in thresholding to make blob mask.
-#' @param final_blob_amp, Contrast factor to multiply smoothed/smudged image. Used in thresholding to make blob mask.
-#' @param test_amount, Optional number of first N images you want to run function on. For troubleshooting/testing/variable calibration purposes.
-#' @param brush_size_blob, Brush size for smudging the dna channel to make blobs
-#' @param sigma_blob, Sigma in Gaussian brush for smudging the dna channel to make blobs
-#' @param cell_aspect_ratio Maximum aspect ratio of blob to be defined as a cell
-
-#' @return cropped SC and foci channels around single cells, regardless of stage
-
-
-auto_crop <- function(img_path,  max_cell_area = 20000, min_cell_area = 7000, mean_pix = 0.08, annotation = "off", blob_factor = 15, bg_blob_factor = 10,  offset = 0.2, final_blob_amp = 10, test_amount = 0,brush_size_blob = 51, sigma_blob = 15, cell_aspect_ratio = 2)
-{
-  file_list <- list.files(img_path)
-  dir.create(paste0(img_path,"/crops"))
-  # input :
-
-  # output : a bunch of output jpegs? Or save them all?
-
-  #BiocManager::install("EBImage")
-  cell_count <- 0
-  image_count <-0
-  antibody1_store <- 0
-  antibody2_store <- 0
-  antibody3_store <- 0
-
-  ## for each image that is *-dna.jpeg,
-  for (img_file in file_list){
-    print(img_file)
-    file_base <- img_file
-    filename_path_test <- paste0(img_path,"/", img_file)
-    img_file <- filename_path_test
-    if(grepl("*DAPI.jpeg$", img_file)){
-      file_DAPI <- img_file
-      image <- readImage(file_DAPI)
-      img_orig_DAPI <- channel(image, "grey")
-      antibody3_store <- 1
-    }
-    if(grepl("*SYCP3.jpeg$", img_file)){
-
-      file_dna <- img_file
-
-      image <- readImage(file_dna)
-      img_orig <- channel(2*image, "grey")
-      antibody1_store <- 1
-    }
-    if(grepl("*MLH3.jpeg$", img_file)){
-      file_foci <- img_file
-      image <- readImage(file_foci)
-      img_orig_foci <- channel(image, "gray")
-      # call functions: get
-      antibody2_store <- 1
-    }
-    if(antibody1_store + antibody2_store + antibody3_store ==3){
-      image_count <- image_count +1
-      if(test_amount != 0 && image_count > test_amount){
-        break
-
-      }
-
-      #### function: blur the image
-      ## call it on img_orig, optional offset
-      blob_th <- get_blobs(img_orig,blob_factor, bg_blob_factor, offset,final_blob_amp,brush_size_blob, sigma_blob)
-
-      blob_label <- bwlabel(blob_th)
-      blob_label <- channel(blob_label, "gray")
-      candidate <- bwlabel(blob_label)
-
-      ## function: remove things that aren't cells
-
-      retained <- keep_cells(candidate, max_cell_area, min_cell_area,cell_aspect_ratio)
-
-      ### crop over each cell
-      ## function: crop around every single viable cell
-      ### crop foci channel here
-      ## Loop over all objects in this final "removed" image
-
-
-      x_final <- computeFeatures.shape(retained)
-      x_final <- data.frame(x_final)
-      OOI_final <- nrow(x_final)
-
-      counter_final <- 0
-      # looping through each object to crop
-      while(counter_final<OOI_final){
-        counter_final <- counter_final+1
-
-
-        cell_count <- cell_count +1
-        crop_single_object(retained,OOI_final,counter_final,img_orig,img_orig_foci,img_orig_DAPI,file_dna,file_foci,file_DAPI,cell_count, mean_pix, annotation, file_base, img_path)
-
-        print("Crop number:")
-        print(cell_count)
-      }
-      antibody1_store <- 0
-      antibody2_store <- 0
-      antibody3_store <- 0
-    }
-
-  }
-
-print("out of")
-print(image_count)
-print("images, we got")
-print(cell_count)
-print("viable cells")
-}
-
-
-### add all the functions used here
-
-#' get_blobs
-#'
-#' Makes mask of all objects bright enough
-#'
-#' @export
-#' @param img_orig Original image
-#'
-#' @param blob_factor, Contrast factor to multiply original image by before smoothing/smudging
-#' @param bg_blob_factor, Contrast factor to multiply original image by to take background. Used prior to thresholding.
-#' @param offset, Pixel value offset from bg_blob_factor. Used in thresholding to make blob mask.
-#' @param final_blob_amp, Contrast factor to multiply smoothed/smudged image. Used in thresholding to make blob mask.
-#' @param brush_size_blob, Brush size for smudging the dna channel to make blobs
-#' @param sigma_blob, Sigma in Gaussian brush for smudging the dna channel to make blobs
-#' @return Mask with cell candidates
-
-#################################### new function ####################################
-get_blobs <- function(img_orig, blob_factor, bg_blob_factor, offset,final_blob_amp, brush_size_blob,sigma_blob){
-
-  # input:
-
-  # output:
-
-  ## subfunction: get signals and make BW mask
-  ### default offset
-  thresh <- blob_factor*img_orig
-  # subfunction: big blur to blobs
-  img_tmp_dna <- img_orig
-  img_tmp <- thresh
-  #w = makeBrush(size = 51, shape = 'gaussian', sigma = 15)
-  w <- makeBrush(size = brush_size_blob, shape = 'gaussian', sigma = sigma_blob)
-  img_flo <- filter2(img_tmp, w)
-  ## default amplification
-  bg <- mean(bg_blob_factor*img_tmp)
-  #if(crop_method == "regular"){
-  blob_th <- final_blob_amp*img_flo > bg + offset
-  #}
-
-  #if(crop_method == "watershed"){
-  #  blob_th = 10*img_flo > bg + offset
-  #}
-  return(blob_th)
-
-}
-
-#################################### new function ####################################
-
-
-#' keep_cells
-#'
-#' Deletes objects in mask which are too small, large, oblong i.e. unlikely to be a cell
-#'
-#' @export
-#' @param candidate Mask of individual cell candidates
-#' @param max_cell_area, Maximum pixel area of a cell candidate
-#' @param min_cell_area, Minimum pixel area of a cell candidate
-#' @param cell_aspect_ratio Maximum aspect ratio of blob to be defined as a cell
-
-#' @return Mask of cell candidates which meet size criteria
-keep_cells <- function(candidate, max_cell_area, min_cell_area, cell_aspect_ratio){
-
-  # delete everything that's too small
-  colorimg<- colorLabels(candidate, normalize = TRUE)
-  x <- computeFeatures.shape(candidate)
-  x <- data.frame(x)
-  OOI <- nrow(x)
-  counter <- 0
-  retained <- candidate
-
-  while(counter<OOI){
-    counter <- counter+1
-    pixel_area <- x$s.area[counter]
-    semi_maj <- x$s.radius.max[counter]
-    semi_min <- x$s.radius.min[counter]
-    # if statement checking if it's the wrong area
-    if(pixel_area> max_cell_area | pixel_area < min_cell_area){
-      retained <- as.numeric(retained)*rmObjects(candidate, counter, reenumerate = TRUE)
-    }
-    ## if statement checking that it's not too long i.e. not at edge.
-    if(semi_maj/semi_min > cell_aspect_ratio & is.na(semi_maj/semi_min)==FALSE){
-      retained <- as.numeric(retained)*rmObjects(candidate, counter, reenumerate = TRUE)
-    }
-
-  }
-  retained <- bwlabel(retained)
-  return(retained)
-}
-
-#################################### new function ####################################
-
-
-#' crop_single_object
-#'
-#' Creates mask for every individual cell candidate in mask
-#'
-#' @export
-#' @param retained Mask of cell candidates which meet size criteria. After smoothing/smudging and thresholding.
-#' @param OOI_final, Objects of interest count. Total number of cell candidates in retained.
-#' @param counter_final, Counter for single cell we are focussing on. Remove all other cells where counter_single not equal to counter_final.
-#' @param img_orig, description
-#' @param img_orig_foci, description
-#' @param img_orig_DAPI, description
-#' @param file_dna, filename of dna channel image
-#' @param file_foci, filename of foci channel image
-#' @param file_DAPI, filename of DAPI channel image
-#' @param cell_count, counter for successful crops around cells
-#' @param mean_pix, Mean pixel intensity of cell crop (in SYCP3 channel) for normalisation
-#' @param annotation, Choice to output pipeline choices (recommended to knit)
-#' @param file_base, filename base common to all three channels i.e. without -MLH3.jpeg etc.
-#' @param img_path, path containing image data to analyse
-
-#'
-
-
-
-#' @return Crops around all candidates in both channels
-#'
-crop_single_object <- function(retained, OOI_final,counter_final,img_orig,img_orig_foci,img_orig_DAPI,file_dna,file_foci,file_DAPI,cell_count, mean_pix, annotation, file_base, img_path){
-  tmp_img <- retained
-  ## have a single object
-  ### delete all other objects
-  counter_single <- 0
-  # looping over all other objects to crop
-  while(counter_single < OOI_final){
-    counter_single <- counter_single + 1
-    # iteratively remove all other objects
-    if(counter_single != counter_final){
-      tmp_img <- as.numeric(tmp_img)*rmObjects(bwlabel(retained), counter_single, reenumerate = TRUE)
-
-    }
-
-
-  }
-  if(annotation == "on"){
-    print("here is the mask of a single cell")
-    display(tmp_img)
-
-  }
-
-  ## function: remove noise
-  noise_gone <- bwlabel(tmp_img)*as.matrix(img_orig)
-  noise_gone_foci <- bwlabel(tmp_img)*as.matrix(img_orig_foci)
-  noise_gone_DAPI <- bwlabel(tmp_img)*as.matrix(img_orig_DAPI)
-  ## first get the row and column list that has a one in it.
-  row_list <- c()
-  col_list <- c()
-  # I think this is quick enough for now.. takes less than 10s...
-  xx <- data.frame(as.numeric(tmp_img))
-  xx <- data.frame(bwlabel(tmp_img))
-  my_matrix <- t(as.matrix(xx))
-  i <- 0
-  ### now loop over matrix
-  for(row in 1:nrow(my_matrix)) {
-    for(col in 1:ncol(my_matrix)) {
-      if(my_matrix[row, col]==1){
-        row_list[i] <- row
-        col_list[i] <- col
-        i <- i+1
-      }
-    }
-  }
-
-  if (length(col_list>1)){
-    cy <- mean(row_list)
-    cx <- mean(col_list)
-
-    x_maj <- max(col_list)
-    x_min <- min(col_list)
-
-    y_maj <- max(row_list)
-    y_min <- min(row_list)
-    ## radius
-    ## total number of pixels given by list length. Find radius of area.
-    ## crops to a square for the moment. can change this.
-    max_r <- max(x_maj-cx,y_maj-cy)
-    crop_r <- floor(max_r)
-    # might want to do this as a matrix
-    top_left_x <- floor(cx-crop_r)
-    top_left_y <- floor(cy-crop_r)
-
-    bottom_left_x <- floor(cx-crop_r)
-    bottom_left_y <- floor(cy+crop_r)
-
-    bottom_right_x <- floor(cx+crop_r)
-    bottom_right_y <- floor(cy+crop_r)
-
-    top_right_x <- floor(cx-crop_r)
-    top_right_y <- floor(cy+crop_r)
-
-    ## crop image
-    ix <- bottom_left_x:bottom_right_x
-    iy <- top_left_y:bottom_left_y
-
-    # determine the dimensions, 2 in this case
-
-    ## cropping part
-    tryCatch({
-      new_img <- noise_gone[ix, iy]
-      ## want all images to have the same mean to 0.1
-      orig_mean <- mean(new_img)
-      mean_factor <- mean_pix/orig_mean
-      new_img <- new_img*mean_factor
-      #file_dna <- tools::file_path_sans_ext(file_dna)
-      print(file_dna)
-      file_dna <- gsub('-SYCP3.jpeg','', file_base)
-      print(file_dna)
-      filename_crop <- paste0(img_path,"/crops/", file_dna,"-crop-",cell_count,"-SYCP3.jpeg")
-      writeImage(new_img, filename_crop)
-
-      new_img_foci <- noise_gone_foci[ix, iy]
-      #file_foci <- tools::file_path_sans_ext(file_foci)
-      file_foci <- gsub('-MLH3.jpeg','', file_foci)
-      filename_crop_foci <- paste0(img_path,"/crops/", file_dna,"-crop-",cell_count,"-MLH3.jpeg")
-      writeImage(new_img_foci, filename_crop_foci)
-
-      new_img_DAPI <- noise_gone_DAPI[ix, iy]
-      #file_foci <- tools::file_path_sans_ext(file_foci)
-      file_DAPI <- gsub('-DAPI.jpeg','', file_DAPI)
-      filename_crop_DAPI <- paste0(img_path,"/crops/", file_dna,"-crop-",cell_count,"-DAPI.jpeg")
-      writeImage(new_img_DAPI, filename_crop_DAPI)
-
-      if(annotation=="on"){
-        print("from the file:")
-        print(file_dna)
-        display(img_orig)
-        print("I cropped this cell:")
-        display(new_img)
-        print("whose cell number is")
-        print(cell_count)
-      }
-
-
-      #### strand related stuff here
-    },
-    error = function(e) {
-      #what should be done in case of exception?
-      str(e) # #prints structure of exception
-      print("couldn't crop it")
-
-    }
-    )
-  }
-
-}
-
-
-#################################### new function ####################################
-

R/auto_crop_fast.R

@@ -24,30 +24,26 @@
 #' @param channel3_string Optional. String appended to the files showing the channel illuminating cell structures. Defaults to DAPI, if third channel == "on".
 #' @param third_channel Optional, defaults to "off".
 #' @param file_ext file extension of your images e.g. tif jpeg or png.
+#' @examples demo_path = paste0(system.file("extdata",package = "synapsis"))
+#' auto_crop_fast(demo_path, annotation = "on", max_cell_area = 30000,
+#' min_cell_area = 7000)
+#' @author Lucy McNeill
 #' @return cropped SC and foci channels around single cells, regardless of stage
 
-
 auto_crop_fast <- function(img_path,  max_cell_area = 20000, min_cell_area = 7000, mean_pix = 0.08, annotation = "off", blob_factor = 15, bg_blob_factor = 10,  offset = 0.2, final_blob_amp = 10, test_amount = 0,brush_size_blob = 51, sigma_blob = 15, channel3_string = "DAPI", channel2_string = "SYCP3", channel1_string = "MLH3", file_ext = "jpeg", third_channel = "off",cell_aspect_ratio = 2)
 {
   file_list <- list.files(img_path)
   dir.create(paste0(img_path,"/crops"))
-  # input :
-
-  # output : a bunch of output jpegs? Or save them all?
-
-  #BiocManager::install("EBImage")
   cell_count <- 0
   image_count <-0
   antibody1_store <- 0
   antibody2_store <- 0
   antibody3_store <- 0
-
   ## for each image that is *-dna.jpeg,
   for (img_file in file_list){
     file_base <- img_file
     filename_path_test <- paste0(img_path,"/",img_file)
     img_file <- filename_path_test
-    #if(grepl("*DAPI.jpeg$", file)){
     if(third_channel == "on"){
       if(grepl(paste0('*',channel3_string,'.',file_ext,'$'),img_file)){
         file_DAPI <- img_file
@@ -56,21 +52,17 @@ auto_crop_fast <- function(img_path,  max_cell_area = 20000, min_cell_area = 700
         antibody3_store <- 1
       }
     }
-    #if(grepl("*SYCP3.jpeg$", file)){
     if(grepl(paste0('*',channel2_string,'.',file_ext,'$'), img_file)){
-
       file_dna <- img_file
       print(file)
       image <- readImage(file_dna)
       img_orig <- channel(2*image, "grey")
       antibody1_store <- 1
     }
-    #if(grepl("*MLH3.jpeg$", file)){
     if(grepl(paste0('*',channel1_string,'.',file_ext,'$'), img_file)){
       file_foci <- img_file
       image <- readImage(file_foci)
       img_orig_foci <- channel(image, "gray")
-      # call functions: get
       antibody2_store <- 1
     }
     if(third_channel == "off"){
@@ -80,19 +72,15 @@ auto_crop_fast <- function(img_path,  max_cell_area = 20000, min_cell_area = 700
       image_count <- image_count +1
       if(test_amount != 0 && image_count > test_amount){
         break
-
       }
-      #### function: blur the image
-      ## call it on img_orig, optional offset
+      #### blur the image with get_blobs. call it on img_orig, optional offset
       blob_th <- get_blobs(img_orig,blob_factor, bg_blob_factor, offset,final_blob_amp,brush_size_blob, sigma_blob)
       blob_label <- bwlabel(blob_th)
       blob_label <- channel(blob_label, "gray")
       candidate <- bwlabel(blob_label)
-      ## function: remove things that aren't cells
+      ## remove things that aren't cells
       retained <- keep_cells(candidate, max_cell_area, min_cell_area,cell_aspect_ratio)
       ### crop over each cell
-      ## function: crop around every single viable cell
-      ### crop foci channel here
       ## Loop over all objects in this final "removed" image
       x_final <- computeFeatures.shape(retained)
       moment_info <- computeFeatures.moment(retained,as.matrix(img_orig))
@@ -114,15 +102,12 @@ auto_crop_fast <- function(img_path,  max_cell_area = 20000, min_cell_area = 700
         else{
           crop_single_object_fast(retained,OOI_final,counter_final,img_orig,img_orig_foci,img_orig_foci,file_dna,file_foci,file_foci,cell_count, mean_pix, annotation, file_base, img_path, r_max, cx, cy,channel3_string,channel2_string,channel1_string,file_ext,third_channel)
         }
-
       }
       antibody1_store <- 0
       antibody2_store <- 0
       antibody3_store <- 0
     }
-
   }
-
 print("out of")
 print(image_count-1)
 print("images, we got")
@@ -130,14 +115,11 @@ print(cell_count)
 print("viable cells")
 }
 
-#################################### new function ####################################
-
 
 #' crop_single_object_fast
 #'
 #' Creates mask for every individual cell candidate in mask
 #'
-#' @export
 #' @param retained Mask of cell candidates which meet size criteria. After smoothing/smudging and thresholding.
 #' @param OOI_final, Objects of interest count. Total number of cell candidates in retained.
 #' @param counter_final, Counter for single cell we are focussing on. Remove all other cells where counter_single not equal to counter_final.
@@ -178,17 +160,11 @@ crop_single_object_fast <- function(retained, OOI_final,counter_final,img_orig,i
       tmp_img <- as.numeric(tmp_img)*rmObjects(bwlabel(retained), counter_single, reenumerate = TRUE)
     }
   }
-
-  ## function: remove noise
   noise_gone <- bwlabel(tmp_img)*as.matrix(img_orig)
   noise_gone_foci <- bwlabel(tmp_img)*as.matrix(img_orig_foci)
   if(third_channel == "on"){
     noise_gone_DAPI <- bwlabel(tmp_img)*as.matrix(img_orig_DAPI)
   }
-
-
-  ### now loop over matrix
-
   ### use the features of tmp_img
   ### here we have the single object. Need to identify its centre value and radius..
   crop_r <- floor(r_max[counter_final])
@@ -197,40 +173,27 @@ crop_single_object_fast <- function(retained, OOI_final,counter_final,img_orig,i
   # might want to do this as a matrix
   top_left_x <- floor(cx-crop_r)
   top_left_y <- floor(cy-crop_r)
-
   bottom_left_x <- floor(cx-crop_r)
   bottom_left_y <- floor(cy+crop_r)
-
   bottom_right_x <- floor(cx+crop_r)
   bottom_right_y <- floor(cy+crop_r)
-
   top_right_x <- floor(cx-crop_r)
   top_right_y <- floor(cy+crop_r)
-
   ## crop image
   ix <- bottom_left_x:bottom_right_x
   iy <- top_left_y:bottom_left_y
 ### cropping finished
-
-  ## we just need ix and iy
-
-
-  ##
-  # determine the dimensions, 2 in this case
-
   ## cropping part
   tryCatch({
     new_img <- noise_gone[ix, iy]
-    ## want all images to have the same mean to 0.1
+    ## want all images to have the same mean (mean_pix)
     orig_mean <- mean(new_img)
     mean_factor <- mean_pix/orig_mean
     new_img <- new_img*mean_factor
-    #file_dna <- tools::file_path_sans_ext(file_dna)
     file_stub <- paste0('-',channel2_string,'.',file_ext)
     file_dna <- gsub(file_stub,'', file_base)
     filename_crop <- paste0(img_path,"/crops/", file_dna,"-crop-",cell_count,file_stub)
     writeImage(new_img, filename_crop)
-
     new_img_foci <- noise_gone_foci[ix, iy]
     file_stub <- paste0('-',channel1_string,'.',file_ext)
     file_foci <- gsub(file_stub,'', file_foci)
@@ -238,7 +201,6 @@ crop_single_object_fast <- function(retained, OOI_final,counter_final,img_orig,i
     writeImage(new_img_foci, filename_crop_foci)
     if(third_channel == "on"){
       new_img_DAPI <- noise_gone_DAPI[ix, iy]
-      #file_DAPI <- gsub('-DAPI.jpeg','', file_DAPI)
       file_stub <- paste0('-',channel3_string,'.',file_ext)
       file_DAPI <- gsub(file_stub,'', file_DAPI)
       filename_crop_DAPI <- paste0(img_path,"/crops/", file_dna,"-crop-",cell_count,file_stub)
@@ -259,21 +221,78 @@ crop_single_object_fast <- function(retained, OOI_final,counter_final,img_orig,i
     #### strand related stuff here
   },
   error = function(e) {
-    #what should be done in case of exception?
-
     if(annotation=="on"){
       #str(e) # #prints structure of exception
       print("couldn't crop it since cell is on the edge. Neglected the following mask of a cell candidate:")
       display(tmp_img)
     }
-
-
   }
   )
+}
 
 
+#' get_blobs
+#'
+#' Makes mask of all objects bright enough
+#'
+#' @param img_orig Original image
+#'
+#' @param blob_factor, Contrast factor to multiply original image by before smoothing/smudging
+#' @param bg_blob_factor, Contrast factor to multiply original image by to take background. Used prior to thresholding.
+#' @param offset, Pixel value offset from bg_blob_factor. Used in thresholding to make blob mask.
+#' @param final_blob_amp, Contrast factor to multiply smoothed/smudged image. Used in thresholding to make blob mask.
+#' @param brush_size_blob, Brush size for smudging the dna channel to make blobs
+#' @param sigma_blob, Sigma in Gaussian brush for smudging the dna channel to make blobs
+#' @return Mask with cell candidates
+
+get_blobs <- function(img_orig, blob_factor, bg_blob_factor, offset,final_blob_amp, brush_size_blob,sigma_blob){
+  thresh <- blob_factor*img_orig
+  # subfunction: big blur to blobs
+  img_tmp_dna <- img_orig