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#____________________________________________________________
# August 2, 2023
#____________________________________________________________
import os
import numpy as np
import gradio as gr
import SimpleITK as sitk
import seaborn as sns
import matplotlib.pyplot as plt
def LoadImage(path_img):
try:
img_itk, voxel_size= Load_itk_image(path_img)
except FileNotFoundError:
print('ERROR: File not found', path_img)
img_numpy = sitk.GetArrayFromImage(img_itk[0])
img_load=img_numpy.transpose()
#remove background = 0
img_load[img_load==0]=np.nan
return img_load
def Load_itk_image(path_img):
#Read header
file_reader = sitk.ImageFileReader()
file_reader.SetFileName(path_img)
file_reader.ReadImageInformation()
dim_total = file_reader.GetSize()
img_vol_ITK = []
if len(dim_total)==4:
dim_vol = (dim_total[0:3])
# make volume for all images
img_load_all = sitk.ReadImage(path_img, sitk.sitkFloat32)
img_vol_ITK = []
for vol_n in range(dim_total[3]):
#Extract vol out of object
size = list(dim_total)
size[3] = 0
index = [0,0,0,vol_n]
extractor = sitk.ExtractImageFilter()
extractor.SetSize(size)
extractor.SetIndex(index)
img_vol_ITK.append(extractor.Execute(img_load_all))
else:
img_ITK = sitk.ReadImage(path_img, sitk.sitkFloat32)
img_vol_ITK.append(img_ITK)
dim_vol = dim_total
#Voxelsize in mm. forth dimension has no meaning in spacing.
voxel_size = img_vol_ITK[0].GetSpacing()
return img_vol_ITK, voxel_size
def NormalizeMinMax(img):
#Normalization for Volumes
minVal = np.nanmin(img)
maxVal = np.nanmax(img)
img_normalized = (img - minVal)*(1/(maxVal- minVal))
return img_normalized
def NormalizePercentile(img, minP, maxP):
#Normalization for Volumes
minVal = np.nanpercentile(img, minP)
maxVal = np.nanpercentile(img, maxP)
img_normalized = (img - minVal)*(1/(maxVal- minVal))
return img_normalized
def NormalizeZScore(img):
#Normalization for Volumes
meanVal = np.nanmean(img)
stdVal = np.nanstd(img)
img_normalized = (img-meanVal)*(1/stdVal)
return img_normalized
def PrepImage(pathName):
img_load = LoadImage(pathName)
img_normalized = NormalizeMinMax(img_load)
img_normalized_per =NormalizePercentile(img_load, 10, 90)
img_normalized_per98 =NormalizePercentile(img_load, 2, 98)
img_normalized_zscore= (NormalizeZScore(img_load) )
return img_load.flatten(),img_normalized.flatten(),img_normalized_per.flatten(),img_normalized_zscore.flatten(), img_normalized_per98.flatten()
def Norm_image(vol_path,normalization_technique):
imglist = []
imgClist=[]
imgNlist=[]
imgNPlist=[]
imgNZlist=[]
imgNPer98list=[]
print(len(vol_path))
for file in vol_path:
pathName = file.name
img_load = LoadImage(pathName)
imglist.append(img_load.flatten())
if 'MinMax' in normalization_technique:
imgNlist.append(NormalizeMinMax(img_load).flatten())
if 'Z-Score' in normalization_technique:
imgNZlist.append(NormalizeZScore(img_load).flatten())
if 'Percentile (2th - 98th)'in normalization_technique:
imgNPer98list.append(NormalizePercentile(img_load, 2, 98).flatten())
if 'Percentile (10th - 90th)' in normalization_technique:
imgNPlist.append(NormalizePercentile(img_load, 10, 90).flatten())
gr.Info('The volumes are loaded succesfully and different normalization techniques are calculated.')
log_scale = False
plt.figure(11)
fig, ax = plt.subplots(1,1)
for i, file in enumerate(vol_path):
sns.histplot(data=imglist[i].flatten(), kde=False, label=os.path.basename(file.name)[0:25], log_scale=log_scale,element="step", fill=False,bins=500,legend=True).set(title='Original')
ax.legend()
plt.savefig("Original.png")
plots=["Original.png"]
if 'MinMax' in normalization_technique:
fig, ax = plt.subplots(1,1)
for i, file in enumerate(vol_path):
sns.histplot(data=imgNlist[i].flatten(), kde=False, label=os.path.basename(file.name)[0:25], log_scale=log_scale,element="step", fill=False,bins=500,legend=True).set(title='Min-Max')
ax.legend()
plt.savefig("MinMax.png")
plots.append("MinMax.png")
if 'Z-Score' in normalization_technique:
fig, ax = plt.subplots(1,1)
for i, file in enumerate(vol_path):
sns.histplot(data=imgNZlist[i].flatten(), kde=False, label=os.path.basename(file.name)[0:25], log_scale=log_scale,element="step", fill=False,bins=500,legend=True).set(title='Z-Score')
ax.legend()
plt.savefig("Zscore.png")
plots.append("Zscore.png")
if 'Percentile (2th - 98th)'in normalization_technique:
fig, ax = plt.subplots(1,1)
for i, file in enumerate(vol_path):
sns.histplot(data=imgNPer98list[i].flatten(), kde=False, label=os.path.basename(file.name)[0:25], log_scale=log_scale,element="step", fill=False,bins=500,legend=True).set(title='Percentile 2-98')
ax.legend()
plt.savefig("Per98.png")
plots.append("Per98.png")
if 'Percentile (10th - 90th)' in normalization_technique:
fig, ax = plt.subplots(1,1)
for i, file in enumerate(vol_path):
sns.histplot(data=imgNPlist[i].flatten(), kde=False, label=os.path.basename(file.name)[0:25], log_scale=log_scale,element="step", fill=False,bins=500,legend=True).set(title='Percentile 10 - 90')
ax.legend()
plt.savefig("Per90.png")
plots.append("Per90.png")
return plots
description = 'You can upload mutiple image volumes (recommonded 3-5). The files get read by ITK so mulitple different file formats are possible (e.g. *.nii , *.nii.gz, jpeg,..). You need to wait until the data is uploaded. \
Once pressing submit the histograms for multiple normalization techniques are calculated. Depending on file size and selected techniques it might take a while to do the calculations. \
\n The uploaded data is not stored and is deleted once the window is closed. '
inputs = [gr.File(file_count="multiple", label=None),gr.CheckboxGroup(["MinMax", "Z-Score", "Percentile (2th - 98th)", "Percentile (10th - 90th)"])]
demo = gr.Interface(fn=Norm_image,
inputs=inputs,
outputs=[gr.Gallery(label="Profiling Dashboard")], #.style(grid=(2,3))],
description=description,
)
demo.launch().queue()
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