问题描述
我使用带有 slicesNumb 的 makie.jl 来可视化 PET/CT 扫描，我有衰减值的 3d 数组，我使用滑块显示热图，并使用滑块更改切片 - 这很好用，我有两个问题
- 我不知道如何能够定义自定义颜色图（基本上我需要能够指定所有高于某个阈值的将是黑色的，所有低于白色的都将具有与衰减值成正比的灰度值）。
2）我希望能够显示在我的图像上显示（技术上的热图）另一个我可以控制透明度 - 像素的 alpha 值 - 以便显示一些注释/PET ...
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
感谢您的帮助！
解决方法
您可以使用 Colors 和 ColorSchemeTools，但您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,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。查看一些可分发的 DICOM 工具以获取示例。
,我最终管理它基本上我加载了存储在 hdf5 中的 3 维数据（我使用 python 从原始加载到 hdf5）
它可以查看横向切片并在将显示在主图像上的蒙版中注释 3d 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},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,:].= 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,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(10,10,0.01),stop=RGBA(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[:,$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,sliderXVal)
#displaying
colorB = layout[1,2]= Colorbar(scene,hm)
GLMakie.translate!(hmB,Vec3f0(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,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,sliceNumb)
maskArr[] = calculateMouseAndSetmask(maskArr,sliceNumb)
end
end #module
和辅助方法
```@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,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",truemax - min_shown_white + 1)]
end
end #module
