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CTtools.py
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CTtools.py
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#!/usr/bin/env python2
# -*- coding: utf-8 -*-
###############################################################################
## Vision Research Laboratory and ##
## Center for Multimodal Big Data Science and Healthcare ##
## University of California at Santa Barbara ##
## ------------------------------------------------------------------------- ##
## ##
## Copyright (c) 2019 ##
## by the Regents of the University of California ##
## All rights reserved ##
## ##
## Redistribution and use in source and binary forms, with or without ##
## modification, are permitted provided that the following conditions are ##
## met: ##
## ##
## 1. Redistributions of source code must retain the above copyright ##
## notice, this list of conditions, and the following disclaimer. ##
## ##
## 2. Redistributions in binary form must reproduce the above copyright ##
## notice, this list of conditions, and the following disclaimer in ##
## the documentation and/or other materials provided with the ##
## distribution. ##
## ##
## ##
## THIS SOFTWARE IS PROVIDED BY <COPYRIGHT HOLDER> "AS IS" AND ANY ##
## EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE ##
## IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR ##
## PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL <COPYRIGHT HOLDER> OR ##
## CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, ##
## EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, ##
## PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR ##
## PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF ##
## LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING ##
## NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS ##
## SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. ##
## ##
## The views and conclusions contained in the software and documentation ##
## are those of the authors and should not be interpreted as representing ##
## official policies, either expressed or implied, of <copyright holder>. ##
###############################################################################
"""
Created on Wed Nov 29 15:49:08 2017
@author: pkao
"""
import SimpleITK as sitk
import numpy as np
from skimage.filters import threshold_otsu
from skimage import measure
from scipy import ndimage
def bone_extracted(ct_img_path):
ct_img = sitk.ReadImage(ct_img_path)
bone_mask_img = sitk.Image(ct_img.GetWidth(), ct_img.GetHeight(), ct_img.GetDepth(), sitk.sitkFloat32)
output_ct_img = sitk.Image(ct_img.GetWidth(), ct_img.GetHeight(), ct_img.GetDepth(), sitk.sitkFloat32)
ct_nda = sitk.GetArrayFromImage(ct_img)
bone_mask_nda = sitk.GetArrayFromImage(bone_mask_img)
output_ct_nda = sitk.GetArrayFromImage(output_ct_img)
bone_pixel = 500
for z in range(ct_nda.shape[0]):
for x in range(ct_nda.shape[1]):
for y in range(ct_nda.shape[2]):
if ct_nda[z, x, y] >= bone_pixel:
output_ct_nda[z, x, y] = ct_nda[z, x, y]
bone_mask_nda[z, x, y] = 1.0;
output_ct_image = sitk.GetImageFromArray(output_ct_nda)
bone_mask_image = sitk.GetImageFromArray(bone_mask_nda)
output_ct_image_name = ct_img_path[:ct_img_path.find('.nii')]+'_skull.nii'
bone_mask_image_name = ct_img_path[:ct_img_path.find('.nii')]+'_skullMask.nii'
output_ct_image.CopyInformation(ct_img)
bone_mask_image.CopyInformation(ct_img)
sitk.WriteImage(output_ct_image, output_ct_image_name)
return output_ct_image_name
def getMaximum3DRegion(binary):
all_labels = measure.label(binary, background = 0)
props = measure.regionprops(all_labels)
areas = [prop.area for prop in props]
maxArea_label = 1+np.argmax(areas)
max_binary = np.float32(all_labels == maxArea_label)
return max_binary
def normalizeCTscan(ct_nda):
if np.amin(ct_nda) < 0:
ct_normalized_nda = ct_nda - np.amin(ct_nda)
ct_normalized_nda = ct_normalized_nda/np.amax(ct_normalized_nda)
return ct_normalized_nda
def otsuThreshoulding(ct_normalized_nda):
thresh = threshold_otsu(ct_normalized_nda)
binary = (ct_normalized_nda > thresh)*1
return binary.astype(np.float32)
def get2Maximum2DRegions(max_binary):
xy_two_largest_binary = np.zeros(max_binary.shape, dtype = np.float32 )
largest_area = np.zeros(max_binary.shape[0])
second_largest_area = np.zeros(max_binary.shape[0])
for i in range(max_binary.shape[0]):
xy_binary = max_binary[i, :, :]
xy_labels = measure.label(xy_binary, background = 0)
xy_props = measure.regionprops(xy_labels)
xy_areas = [prop.area for prop in xy_props]
#print xy_areas
if xy_areas == []:
continue
elif len(xy_areas) == 1:
largest_area[i] = xy_areas[0]
second_largest_area[i] = 0.0
largest_label = xy_areas.index(largest_area[i]) + 1
xy_two_largest_binary[i, :, :] = xy_labels == largest_label
else:
xy_areas_sorted = sorted(xy_areas)
largest_area[i] = xy_areas_sorted[-1]
second_largest_area[i] = xy_areas_sorted[-2]
largest_label = xy_areas.index(largest_area[i]) + 1
second_largest_label = xy_areas.index(second_largest_area[i])+1
xy_largest_binary = xy_labels == largest_label
xy_second_largest_binary = xy_labels == second_largest_label
xy_two_largest_binary[i, :, :] = np.float32(np.logical_or(xy_largest_binary, xy_second_largest_binary))
return xy_two_largest_binary
def get1Maximum2DRegion(max_second_binary):
new_binary = np.zeros(max_second_binary.shape, dtype = np.float32)
for i in range(max_second_binary.shape[0]):
xy_binary = max_second_binary[i,:,:]
xy_labels = measure.label(xy_binary)
xy_props = measure.regionprops(xy_labels)
xy_areas = [prop.area for prop in xy_props]
#print i, xy_areas_1
if xy_areas == []:
continue
else:
max_area_label = 1 + np.argmax(xy_areas)
new_binary[i,:,:] = np.float32(xy_labels == max_area_label)
return new_binary
def imageOpening2D(max_second_binary, structure=np.ones((15, 15))):
new_max_second_binary = np.zeros(max_second_binary.shape, dtype = np.float32)
for i in range(max_second_binary.shape[0]):
new_max_second_binary[i,:,:] = ndimage.binary_opening(max_second_binary[i,:,:].astype(int), structure=structure).astype(np.float32)
return new_max_second_binary
def removeCTscandevice(ct_img_path):
ct_img = sitk.ReadImage(ct_img_path)
ct_nda = sitk.GetArrayFromImage(ct_img)
ct_normalized_nda = normalizeCTscan(ct_nda)
binary = otsuThreshoulding(ct_normalized_nda)
max_binary = getMaximum3DRegion(binary)
xy_two_largest_binary = get2Maximum2DRegions(max_binary)
max_second_binary = getMaximum3DRegion(xy_two_largest_binary)
new_binary = get1Maximum2DRegion(max_second_binary)
new_max_second_bindary = imageOpening2D(new_binary)
new_max_binary = getMaximum3DRegion(new_max_second_bindary)
woCTscan_mask_image = sitk.GetImageFromArray(new_max_binary)
woCTscan_mask_image.CopyInformation(ct_img)
woCTscan_mask_image_name = ct_img_path[:ct_img_path.find('.nii')]+'_woCTscanMask.nii'
output_ct_image = sitk.GetImageFromArray(ct_nda * new_max_binary)
output_ct_image.CopyInformation(ct_img)
output_ct_image_name = ct_img_path[:ct_img_path.find('.nii')]+'_woCTscan.nii'
sitk.WriteImage(output_ct_image, output_ct_image_name)
return output_ct_image_name