分割环，即 R 中的非完整对象（在 EBIimage 或其他中）
Segmenting rings i.e. non-full objects in R (in EBIimage or other)
我依靠边缘检测（而不是颜色检测）从血细胞中提取特征。原始图像看起来像：
我正在使用 R EBImage 包 运行 一个 sobel + 低通滤波器来得到这样的东西：
library(EBImage)
library(data.table)
img <- readImage("6hr-007-DIC.tif")
#plot(img)
#print(img, short = T)
# 1. define filter for edge detection
hfilt <- matrix(c(1, 2, 1, 0, 0, 0, -1, -2, -1), nrow = 3) # sobel
# rotate horizontal filter to obtain vertical filter
vfilt <- t(hfilt)
# get horizontal and vertical edges
imgH <- filter2(img, hfilt, boundary="replicate")
imgV <- filter2(img, vfilt, boundary="replicate")
# combine edge pixel data to get overall edge data
hdata <- imageData(imgH)
vdata <- imageData(imgV)
edata <- sqrt(hdata^2 + vdata^2)
# transform edge data to image
imgE <- Image(edata)
#print(display(combine(img, imgH, imgV, imgE), method = "raster", all = T))
display(imgE, method = "raster", all = T)
# 2. Enhance edges with low pass filter
hfilt <- matrix(c(1, 1, 1, 1, 1, 1, 1, 1, 1), nrow = 3) # low pass
# rotate horizontal filter to obtain vertical filter
vfilt <- t(hfilt)
# get horizontal and vertical edges
imgH <- filter2(imgE, hfilt, boundary="replicate")
imgV <- filter2(imgE, vfilt, boundary="replicate")
# combine edge pixel data to get overall edge data
hdata <- imageData(imgH)
vdata <- imageData(imgV)
edata <- sqrt(hdata^2 + vdata^2)
# transform edge data to image
imgE <- Image(edata)
plot(imgE)
我想知道是否有任何方法可以填充大环（血细胞）中的孔，使其成为有点像实心体：
（显然这不是同一张图片，但想象一下最后一张图片只是从边缘开始的。）
然后我想使用 EBImage 包中的 computeFeatures() 方法（据我所知，它只适用于实体）
EDIT 多一点代码来提取带有 "connections" 对象的内部边界。附加代码包括定义分段单元格的凸包和创建填充掩码。
简短的回答是 fillHull 和 floodFill 可能有助于填充具有明确边界的单元格。
下面较长的（已编辑）答案提出了一种可能有用的 floodFill 方法。您从低对比度 DIC 图像中提取信息做得很好，但更多的图像处理可能会有所帮助，例如 "flat-field correction" 用于嘈杂的 DIC 图像。这个 Wikipedia page 中描述了原理，但是一个简单的实现会产生奇迹。此处建议的编码解决方案需要用户与 select 个单元格进行交互。这不是一个强有力的方法。尽管如此，也许更多的图像处理与定位细胞的代码相结合是可行的。最后，细胞内部被分割并可用于 computeFeatures.
的分析
代码从经过阈值处理的图像开始（已修剪边缘并转换为二进制）。
# Set up plots for 96 dpi images
library(EBImage)
dm <- dim(img2)/96
dev.new(width = dm[1], height = dm[2])
# Low pass filter with gblur and make binary
xb <- gblur(img2, 3)
xt <- thresh(xb, offset = 0.0001)
plot(xt) # thresh.jpg
# dev.print(jpeg, "thresh.jpg", width = dm[1], unit = "in", res = 96)
# Keep only "large" objects
xm <- bwlabel(xt)
FS <- computeFeatures.shape(xm)
sel <- which(FS[,"s.area"] < 800)
xe <- rmObjects(xm, sel)
# Make binary again and plot
xe <- thresh(xe)
plot(xe) # trimmed.jpg
# dev.print(jpeg, "trimmed.jpg", width = dm[1], unit = "in", res = 96)
# Choose cells with intact interiors
# This is done by hand here but with more pre-processing, it may be
# possible to have the image suitable for more automated analysis...
pp <- locator(type = "p", pch = 3, col = 2) # marked.jpg
# dev.print(jpeg, "marked.jpg", width = dm[1], unit = "in", res = 96)
# Fill interior of each cell with a unique integer
myCol <- seq_along(pp$x) + 1
xf1 <- floodFill(xe, do.call(rbind, pp), col = myCol)
# Discard original objects from threshold (value = 1) and see
cells1 <- rmObjects(xf1, 1)
plot(colorLabels(cells1))
# dev.print(jpeg, "cells1.jpg", width = dm[1], unit = "in", res = 96)
我需要引入算法来连接顶点之间的整数点并填充 凸 多边形。此处的代码实现了 Bresenham 算法，并使用仅适用于凸（简单）多边形的简单多边形填充例程。
#
# Bresenham's balanced integer line drawing algorithm
#
bresenham <- function(x, y = NULL, close = TRUE)
{
# accept any coordinate structure
v <- xy.coords(x = x, y = y, recycle = TRUE, setLab = FALSE)
if (!all(is.finite(v$x), is.finite(v$y)))
stop("finite coordinates required")
v[1:2] <- lapply(v[1:2], round) # Bresenham's algorithm IS for integers
nx <- length(v$x)
if (nx == 1) return(list(x = v$x, y = v$y)) # just one point
if (nx > 2 && close == TRUE) { # close polygon by replicating 1st point
v$x <- c(v$x, v$x[1])
v$y <- c(v$y, v$y[1])
nx <- nx + 1
}
# collect result in 'ans, staring with 1st point
ans <- lapply(v[1:2], "[", 1)
# process all vertices in pairs
for (i in seq.int(nx - 1)) {
x <- v$x[i] # coordinates updated in x, y
y <- v$y[i]
x.end <- v$x[i + 1]
y.end <- v$y[i + 1]
dx <- abs(x.end - x); dy <- -abs(y.end - y)
sx <- ifelse(x < x.end, 1, -1)
sy <- ifelse(y < y.end, 1, -1)
err <- dx + dy
# process one segment
while(!(isTRUE(all.equal(x, x.end)) && isTRUE(all.equal(y, y.end)))) {
e2 <- 2 * err
if (e2 >= dy) { # increment x
err <- err + dy
x <- x + sx
}
if (e2 <= dx) { # increment y
err <- err + dx
y <- y + sy
}
ans$x <- c(ans$x, x)
ans$y <- c(ans$y, y)
}
}
# remove duplicated points (typically 1st and last)
dups <- duplicated(do.call(cbind, ans), MARGIN = 1)
return(lapply(ans, "[", !dups))
}
以及查找简单多边形内点的简单例程。
#
# Return x,y integer coordinates of the interior of a CONVEX polygon
#
cPolyFill <- function(x, y = NULL)
{
p <- xy.coords(x, y = y, recycle = TRUE, setLab = FALSE)
p[1:2] <- lapply(p[1:2], round)
nx <- length(p$x)
if (any(!is.finite(p$x), !is.finite(p$y)))
stop("finite coordinates are needed")
yc <- seq.int(min(p$y), max(p$y))
xlist <- lapply(yc, function(y) sort(seq.int(min(p$x[p$y == y]), max(p$x[p$y == y]))))
ylist <- Map(rep, yc, lengths(xlist))
ans <- cbind(x = unlist(xlist), y = unlist(ylist))
return(ans)
}
现在这些可以与 ocontour() 和 chull() 一起使用来创建和填充每个分段单元格的凸包。这"fixes"那些被入侵的小区
# Create convex hull mask
oc <- ocontour(cells1) # for all points along perimeter
oc <- lapply(oc, function(v) v + 1) # off-by-one flaw in ocontour
sel <- lapply(oc, chull) # find points that define convex hull
xh <- Map(function(v, i) rbind(v[i,]), oc, sel) # new vertices for convex hull
oc2 <- lapply(xh, bresenham) # perimeter points along convex hull
# Collect interior coordinates and fill
coords <- lapply(oc2, cPolyFill)
cells2 <- Image(0, dim = dim(cells1))
for(i in seq_along(coords))
cells2[coords[[i]]] <- i # blank image for mask
xf2 <- xe
for (i in seq_along(coords))
xf2[coords[[i]]] <- i # early binary mask
# Compare before and after
img <- combine(colorLabels(xf1), colorLabels(cells1),
colorLabels(xf2), colorLabels(cells2))
plot(img, all = T, nx = 2)
labs <- c("xf1", "cells1", "xf2", "cells2")
ix <- c(0, 1, 0, 1)
iy <- c(0, 0, 1, 1)
text(dm[1]*96*(ix + 0.05), 96*dm[2]*(iy + 0.05), labels = labs,
col = "white", adj = c(0.05,1))
# dev.print(jpeg, "final.jpg", width = dm[1], unit = "in", res = 96)
我依靠边缘检测（而不是颜色检测）从血细胞中提取特征。原始图像看起来像：
我正在使用 R EBImage 包 运行 一个 sobel + 低通滤波器来得到这样的东西：
library(EBImage)
library(data.table)
img <- readImage("6hr-007-DIC.tif")
#plot(img)
#print(img, short = T)
# 1. define filter for edge detection
hfilt <- matrix(c(1, 2, 1, 0, 0, 0, -1, -2, -1), nrow = 3) # sobel
# rotate horizontal filter to obtain vertical filter
vfilt <- t(hfilt)
# get horizontal and vertical edges
imgH <- filter2(img, hfilt, boundary="replicate")
imgV <- filter2(img, vfilt, boundary="replicate")
# combine edge pixel data to get overall edge data
hdata <- imageData(imgH)
vdata <- imageData(imgV)
edata <- sqrt(hdata^2 + vdata^2)
# transform edge data to image
imgE <- Image(edata)
#print(display(combine(img, imgH, imgV, imgE), method = "raster", all = T))
display(imgE, method = "raster", all = T)
# 2. Enhance edges with low pass filter
hfilt <- matrix(c(1, 1, 1, 1, 1, 1, 1, 1, 1), nrow = 3) # low pass
# rotate horizontal filter to obtain vertical filter
vfilt <- t(hfilt)
# get horizontal and vertical edges
imgH <- filter2(imgE, hfilt, boundary="replicate")
imgV <- filter2(imgE, vfilt, boundary="replicate")
# combine edge pixel data to get overall edge data
hdata <- imageData(imgH)
vdata <- imageData(imgV)
edata <- sqrt(hdata^2 + vdata^2)
# transform edge data to image
imgE <- Image(edata)
plot(imgE)
我想知道是否有任何方法可以填充大环（血细胞）中的孔，使其成为有点像实心体：
（显然这不是同一张图片，但想象一下最后一张图片只是从边缘开始的。）
然后我想使用 EBImage 包中的 computeFeatures() 方法（据我所知，它只适用于实体）
EDIT 多一点代码来提取带有 "connections" 对象的内部边界。附加代码包括定义分段单元格的凸包和创建填充掩码。
简短的回答是 fillHull 和 floodFill 可能有助于填充具有明确边界的单元格。
下面较长的（已编辑）答案提出了一种可能有用的 floodFill 方法。您从低对比度 DIC 图像中提取信息做得很好，但更多的图像处理可能会有所帮助，例如 "flat-field correction" 用于嘈杂的 DIC 图像。这个 Wikipedia page 中描述了原理，但是一个简单的实现会产生奇迹。此处建议的编码解决方案需要用户与 select 个单元格进行交互。这不是一个强有力的方法。尽管如此，也许更多的图像处理与定位细胞的代码相结合是可行的。最后，细胞内部被分割并可用于 computeFeatures.
代码从经过阈值处理的图像开始（已修剪边缘并转换为二进制）。
# Set up plots for 96 dpi images
library(EBImage)
dm <- dim(img2)/96
dev.new(width = dm[1], height = dm[2])
# Low pass filter with gblur and make binary
xb <- gblur(img2, 3)
xt <- thresh(xb, offset = 0.0001)
plot(xt) # thresh.jpg
# dev.print(jpeg, "thresh.jpg", width = dm[1], unit = "in", res = 96)
# Keep only "large" objects
xm <- bwlabel(xt)
FS <- computeFeatures.shape(xm)
sel <- which(FS[,"s.area"] < 800)
xe <- rmObjects(xm, sel)
# Make binary again and plot
xe <- thresh(xe)
plot(xe) # trimmed.jpg
# dev.print(jpeg, "trimmed.jpg", width = dm[1], unit = "in", res = 96)
# Choose cells with intact interiors
# This is done by hand here but with more pre-processing, it may be
# possible to have the image suitable for more automated analysis...
pp <- locator(type = "p", pch = 3, col = 2) # marked.jpg
# dev.print(jpeg, "marked.jpg", width = dm[1], unit = "in", res = 96)
# Fill interior of each cell with a unique integer
myCol <- seq_along(pp$x) + 1
xf1 <- floodFill(xe, do.call(rbind, pp), col = myCol)
# Discard original objects from threshold (value = 1) and see
cells1 <- rmObjects(xf1, 1)
plot(colorLabels(cells1))
# dev.print(jpeg, "cells1.jpg", width = dm[1], unit = "in", res = 96)
我需要引入算法来连接顶点之间的整数点并填充 凸 多边形。此处的代码实现了 Bresenham 算法，并使用仅适用于凸（简单）多边形的简单多边形填充例程。
#
# Bresenham's balanced integer line drawing algorithm
#
bresenham <- function(x, y = NULL, close = TRUE)
{
# accept any coordinate structure
v <- xy.coords(x = x, y = y, recycle = TRUE, setLab = FALSE)
if (!all(is.finite(v$x), is.finite(v$y)))
stop("finite coordinates required")
v[1:2] <- lapply(v[1:2], round) # Bresenham's algorithm IS for integers
nx <- length(v$x)
if (nx == 1) return(list(x = v$x, y = v$y)) # just one point
if (nx > 2 && close == TRUE) { # close polygon by replicating 1st point
v$x <- c(v$x, v$x[1])
v$y <- c(v$y, v$y[1])
nx <- nx + 1
}
# collect result in 'ans, staring with 1st point
ans <- lapply(v[1:2], "[", 1)
# process all vertices in pairs
for (i in seq.int(nx - 1)) {
x <- v$x[i] # coordinates updated in x, y
y <- v$y[i]
x.end <- v$x[i + 1]
y.end <- v$y[i + 1]
dx <- abs(x.end - x); dy <- -abs(y.end - y)
sx <- ifelse(x < x.end, 1, -1)
sy <- ifelse(y < y.end, 1, -1)
err <- dx + dy
# process one segment
while(!(isTRUE(all.equal(x, x.end)) && isTRUE(all.equal(y, y.end)))) {
e2 <- 2 * err
if (e2 >= dy) { # increment x
err <- err + dy
x <- x + sx
}
if (e2 <= dx) { # increment y
err <- err + dx
y <- y + sy
}
ans$x <- c(ans$x, x)
ans$y <- c(ans$y, y)
}
}
# remove duplicated points (typically 1st and last)
dups <- duplicated(do.call(cbind, ans), MARGIN = 1)
return(lapply(ans, "[", !dups))
}
以及查找简单多边形内点的简单例程。
#
# Return x,y integer coordinates of the interior of a CONVEX polygon
#
cPolyFill <- function(x, y = NULL)
{
p <- xy.coords(x, y = y, recycle = TRUE, setLab = FALSE)
p[1:2] <- lapply(p[1:2], round)
nx <- length(p$x)
if (any(!is.finite(p$x), !is.finite(p$y)))
stop("finite coordinates are needed")
yc <- seq.int(min(p$y), max(p$y))
xlist <- lapply(yc, function(y) sort(seq.int(min(p$x[p$y == y]), max(p$x[p$y == y]))))
ylist <- Map(rep, yc, lengths(xlist))
ans <- cbind(x = unlist(xlist), y = unlist(ylist))
return(ans)
}
现在这些可以与 ocontour() 和 chull() 一起使用来创建和填充每个分段单元格的凸包。这"fixes"那些被入侵的小区
# Create convex hull mask
oc <- ocontour(cells1) # for all points along perimeter
oc <- lapply(oc, function(v) v + 1) # off-by-one flaw in ocontour
sel <- lapply(oc, chull) # find points that define convex hull
xh <- Map(function(v, i) rbind(v[i,]), oc, sel) # new vertices for convex hull
oc2 <- lapply(xh, bresenham) # perimeter points along convex hull
# Collect interior coordinates and fill
coords <- lapply(oc2, cPolyFill)
cells2 <- Image(0, dim = dim(cells1))
for(i in seq_along(coords))
cells2[coords[[i]]] <- i # blank image for mask
xf2 <- xe
for (i in seq_along(coords))
xf2[coords[[i]]] <- i # early binary mask
# Compare before and after
img <- combine(colorLabels(xf1), colorLabels(cells1),
colorLabels(xf2), colorLabels(cells2))
plot(img, all = T, nx = 2)
labs <- c("xf1", "cells1", "xf2", "cells2")
ix <- c(0, 1, 0, 1)
iy <- c(0, 0, 1, 1)
text(dm[1]*96*(ix + 0.05), 96*dm[2]*(iy + 0.05), labels = labs,
col = "white", adj = c(0.05,1))
# dev.print(jpeg, "final.jpg", width = dm[1], unit = "in", res = 96)