Plot 如何创建用于显示Ct扫描的julia配色方案Makie.jl

我使用makie.jl和slicesNumb对PET/CT扫描进行可视化，我有衰减值的3d阵列，我用滑块显示热图，并改变切片-这很好，我有两个问题

我不知道如何定义自定义颜色贴图（基本上，我需要能够指定高于某个阈值的所有颜色都是黑色，低于白色的所有颜色，以及介于两者之间的值的灰度值与衰减值成比例）

2） 我希望能够显示在我的图像上（快速热图），我可以控制透明度-像素的alpha值-以便显示一些注释/PET

代码，但没有这两个功能，以及它的外观

using GLMakie

```@doc
simple display of single image - only in transverse plane
```
function singleCtScanDisplay(arr ::Array{Number, 3}) 
  

  fig = Figure()
  sl_x = Slider(fig[2, 1], range = 1:1:size(arr)[3], startvalue = 40)
  ax = Axis(fig[1, 1])
  hm = heatmap!(ax, lift(idx-> arr[:,:, floor(idx)], sl_x.value) ,colormap = :grays)
  Colorbar(fig[1, 2], hm)

  
  fig

end

---

谢谢你的帮助

您可以使用

颜色

和

颜色方案工具

，但需要根据阈值添加方案的顶部和底部

using Colors, ColorSchemeTools

truemin = 0
truemax = 600
max_shown_black = 20
min_shown_white = 500

data = rand(truemin:truemax, (500, 500, 20))

grayscheme =  [fill(colorant"black", max_shown_black - truemin + 1);
               collect(make_colorscheme(identity, identity, identity,
                   length = min_shown_white - max_shown_black - 1));
               fill(colorant"white", truemax - min_shown_white + 1)]

 

---

为了控制alpha，我会添加一个带有alpha滑块的弹出窗口。以一些可分发的DICOM工具为例。

我最终管理了它，基本上我加载了存储在hdf5中的三维数据（我使用python从原始数据加载到hdf5中）

它可以查看横向切片，并在将显示在主图像上的遮罩中注释三维路径

exmpleH = @spawnat persistenceWorker Main.h5manag.getExample()
minimumm = -1000
maximumm = 2000
arrr= fetch(exmpleH)
imageDim = size(arrr)
using GLMakie
maskArr = Observable(BitArray(undef, imageDim))
MyImgeViewer.singleCtScanDisplay(arrr, maskArr,minimumm, maximumm)

现在定义所需的模块

```@doc
functions responsible for displaying medical image Data
```
using DrWatson
@quickactivate "Probabilistic medical segmentation"


module MyImgeViewer

using GLMakie
using Makie
#using GeometryBasics
using GeometricalPredicates
using ColorTypes
using Distributed
using GLMakie
using Main.imageViewerHelper
using Main.workerNumbers
## getting id of workers 

```@doc
simple display of single image - only in transverse plane we are adding also a mask  that 
arrr - main 3 dimensional data representing medical image for example in case of CT each voxel represents value of X ray attenuation
minimumm, maximumm - approximately minimum and maximum values we can have in our image
```
function singleCtScanDisplay(arrr ::Array{Number, 3}, maskArr , minimumm, maximumm) 
#we modify 2 pixels just in order to make the color range constant so slices will be displayed in the same windows
arrr[1,1,:].= minimumm 
arrr[2,1,:].= maximumm 
  

imageDim = size(arrr) # dimenstion of the primary image for example CT scan
slicesNumb =imageDim[3] # number of slices 

#defining layout variables
scene, layout = GLMakie.layoutscene(resolution = (600, 400))
ax1 = layout[1, 1] = GLMakie.Axis(scene, backgroundcolor = :transparent)
ax2 = layout[1, 1] = GLMakie.Axis(scene, backgroundcolor = :transparent)

#control widgets
sl_x =layout[2, 1]= GLMakie.Slider(scene, range = 1:1: slicesNumb , startvalue = slicesNumb/2 )
sliderXVal = sl_x.value


#color maps
cmwhite = cgrad(range(RGBA(10,10,10,0.01), stop=RGBA(0,0,255,0.4), length=10000));
greyss = createMedicalImageColorSchemeB(200,-200,maximumm, minimumm )
####heatmaps

#main heatmap that holds for example Ct scan
currentSliceMain = GLMakie.@lift(arrr[:,:, convert(Int32,$sliderXVal)])
hm = GLMakie.heatmap!(ax1, currentSliceMain ,colormap = greyss) 

#helper heatmap designed to respond to both changes in slider and changes in the bit matrix
currentSliceMask = GLMakie.@lift($maskArr[:,:, convert(Int32,$sliderXVal)])
hmB = GLMakie.heatmap!(ax1, currentSliceMask ,colormap = cmwhite) 

#adding ability to be able to add information to mask  where we clicked so in casse of mit matrix we will set the point where we clicked to 1 
indicatorC(ax1,imageDim,scene,maskArr,sliderXVal)

#displaying
colorB = layout[1,2]= Colorbar(scene, hm)
GLMakie.translate!(hmB, Vec3f0(0,0,5))  

scene

end

```@doc
inspired by   https://github.com/JuliaPlots/Makie.jl/issues/810
Generaly thanks to this function  the viewer is able to respond to clicking on the slices and records it in the supplied 3 dimensional AbstractArray
  ax - Axis which store our heatmap slices which we want to observe wheather user clicked on them and where
  dims - dimensions of  main image for example CT
  sc - Scene where our axis is
  maskArr - the 3 dimensional bit array  that has exactly the same dimensions as main Array storing image 
  sliceNumb - represents on what slide we are on currently on - ussually it just give information from slider 
```
function indicatorC(ax::Axis,dims::Tuple{Int64, Int64, Int64},sc::Scene,maskArr,sliceNumb::Observable{Any})
  register_interaction!(ax, :indicator) do event::GLMakie.MouseEvent, axis
  if event.type === MouseEventTypes.leftclick
    println("clicked")
    #@async begin
      #appropriately modyfing wanted pixels in mask array
   @async calculateMouseAndSetmaskWrap(maskArr, event,sc,dims,sliceNumb)            
    #  
    #  
    #  println("fetched" + fetch(maskA))

    #  finalize(maskA)
    #end
    return true
    #print("xMouse: $(xMouse)  yMouse: $(yMouse)   compBoxWidth: $(compBoxWidth)  compBoxHeight: $(compBoxHeight)   calculatedXpixel: $(calculatedXpixel)  calculatedYpixel: $(calculatedYpixel)      pixelsNumbInX  $(pixelsNumbInX)         ") 
  end
  
end
end
```@doc
wrapper for calculateMouseAndSetmask  - from imageViewerHelper module
  given mouse event modifies mask accordingly
  maskArr - the 3 dimensional bit array  that has exactly the same dimensions as main Array storing image 
  event - mouse event passed from Makie
  sc - scene we are using in Makie
  ```
function calculateMouseAndSetmaskWrap(maskArr, event,sc,dims,sliceNumb) 
  maskArr[] = calculateMouseAndSetmask(maskArr, event,sc,dims,sliceNumb)
end


end #module

和助手方法

```@doc
functions responsible for helping in image viewer - those functions are  meant to be invoked on separate process
- in parallel
```
using DrWatson
@quickactivate "Probabilistic medical segmentation"

module imageViewerHelper
using Documenter
using ColorTypes
using Colors, ColorSchemeTools
using Makie

export calculateMouseAndSetmask
export createMedicalImageColorSchemeB
# using AbstractPlotting
```@doc
  given mouse event modifies mask accordingly
  maskArr - the 3 dimensional bit array  that has exactly the same dimensions as main Array storing image 
  event - mouse event passed from Makie
  sc - scene we are using in Makie
  ```
function calculateMouseAndSetmask(maskArr, event,sc,dims,sliceNumb) 
  #position from top left corner 
  xMouse= Makie.to_world(sc,event.data)[1]
  yMouse= Makie.to_world(sc,event.data)[2]
  #data about height and width in layout
  compBoxWidth = 510 
  compBoxHeight = 510 
  #image dimensions - number of pixels  from medical image for example ct scan
  pixelsNumbInX =dims[1]
  pixelsNumbInY =dims[2]
  #calculating over which image pixel we are
  calculatedXpixel =convert(Int32, round( (xMouse/compBoxWidth)*pixelsNumbInX) )
  calculatedYpixel =  convert(Int32,round( (yMouse/compBoxHeight)*pixelsNumbInY ))
  sliceNumbConv =convert(Int32,round( sliceNumb[] ))
  #appropriately modyfing wanted pixels in mask array
  return markMaskArrayPatch( maskArr ,CartesianIndex(calculatedXpixel, calculatedYpixel, sliceNumbConv  ),2)
end



```@doc
  maskArr - the 3 dimensional bit array  that has exactly the same dimensions as main Array storing image 
  point - cartesian coordinates of point around which we want to modify the 3 dimensional array from 0 to 1

```
function markMaskArrayPatch(maskArr, pointCart::CartesianIndex{3}, patchSize ::Int64)

  ones = CartesianIndex(patchSize,patchSize,patchSize) # cartesian 3 dimensional index used for calculations to get range of the cartesian indicis to analyze
  maskArrB = maskArr[]
  for J in (pointCart-ones):(pointCart+ones)
    diff = J - pointCart # diffrence between dimensions relative to point of origin
      if cartesianTolinear(diff) <= patchSize
        maskArrB[J]=1
      end
      end
return maskArrB
end
```@doc
works only for 3d cartesian coordinates
  cart - cartesian coordinates of point where we will add the dimensions ...
```
function cartesianTolinear(pointCart::CartesianIndex{3}) :: Int16
   abs(pointCart[1])+ abs(pointCart[2])+abs(pointCart[3])
end

```@doc
creating grey scheme colors for proper display of medical image mainly CT scan
min_shown_white - max_shown_black range over which the gradint of greys will be shown
truemax - truemin the range of values in the image for which we are creating the scale
```
#taken from https://stackoverflow.com/questions/67727977/how-to-create-julia-color-scheme-for-displaying-ct-scan-makie-jl/67756158#67756158

function createMedicalImageColorSchemeB(min_shown_white,max_shown_black,truemax,truemin ) ::Vector{Any} 
# println("max_shown_black - truemin + 1")
# println(max_shown_black - truemin + 1)
# println(" min_shown_white - max_shown_black - 1")
# println( min_shown_white - max_shown_black - 1)
# println("truemax - min_shown_white + 1")
# println(truemax - min_shown_white + 1)

return  [fill(colorant"black", max_shown_black - truemin + 1);
               collect(make_colorscheme(identity, identity, identity,
                   length = min_shown_white - max_shown_black - 1));
               fill(colorant"white", truemax - min_shown_white + 1)]

end





end #module

``@doc
在图像查看器中负责帮助的函数-这些函数将在单独的进程中调用
-并行
```
使用DrWatson
@快速激活“概率医学分段”
模块图像查看器帮助器
使用文档管理器
使用颜色类型
使用颜色、颜色模式工具
使用Makie
导出calculateMouseAndSetmask
导出createMedicalImageColorSchemeB
#使用抽象绘图
```@医生
给定的鼠标事件会相应地修改掩码
maskArr-与存储图像的主阵列的尺寸完全相同的三维位阵列
事件-从Makie传递的鼠标事件
sc-我们在Makie中使用的场景
```
函数calculateMouseAndSetmask（maskArr、event、sc、dims、sliceNumb）
#位置从左上角开始
xMouse=Makie.to_world（sc，event.data）[1]
yMouse=Makie.to_world（sc，event.data）[2]
#关于布局中的高度和宽度的数据
compBoxWidth=510
compBoxHeight=510
#图像尺寸-医学图像的像素数，例如ct扫描
pixelsumbinx=dims[1]
像素亮度=dims[2]
#计算我们所使用的图像像素
calculatedXpixel=转换（Int32，圆形（（xMouse/compBoxWidth）*像素Mbinx））
calculatedYpixel=转换（Int32，圆形（（Y使用/compBoxHeight）*像素MB））
sliceNumbConv=convert（Int32，圆形（sliceNumb[]））
#在遮罩阵列中适当地修改所需像素
返回markMaskArrayPatch（maskArr，CartesianIndex（calculatedXpixel，calculatedYpixel，sliceNumbConv），2）
结束
```@医生
maskArr-与存储图像的主阵列的尺寸完全相同的三维位阵列
点-要将三维数组从0修改为1的点的笛卡尔坐标
```
函数markMaskArrayPatch（maskArr，pointCart:：CartesianIndex{3}，patchSize:：Int64）
ones=笛卡尔指数（patchSize，patchSize，patchSize）#笛卡尔三维指数，用于计算以获得要分析的笛卡尔指数范围
maskArrB=maskArr[]
对于J英寸（点车1个）：（点车+1个）
diff=J-点车#相对于原点的尺寸差异
如果cartesianTolinear（diff）可能有ColorSchemes.jl和ColorSchemeTools.jl的帮助？