I'm working on detecting the brain tumor by using python. I need some help for the segmentation process in my code. I want to remove the skull part in the brain MRI. I'm using the watershed algorithm to define the skull in the MRI images. however, the tumor region is also shaded by the code. how can I change the threshold detected by the code in order to keep the whole brain region untouched by the algorithm. the picture shown below is a sample of what i want to get. ` import numpy as np import cv2 from matplotlib import pyplot as plt from skimage.morphology import extrema from skimage.morphology import watershed as skwater
def ShowImage(title,img,ctype):
plt.figure(figsize=(10, 10))
if ctype=='bgr':
b,g,r = cv2.split(img) # get b,g,r
rgb_img = cv2.merge([r,g,b]) # switch it to rgb
plt.imshow(rgb_img)
elif ctype=='hsv':
rgb = cv2.cvtColor(img,cv2.COLOR_HSV2RGB)
plt.imshow(rgb)
elif ctype=='gray':
plt.imshow(img,cmap='gray')
elif ctype=='rgb':
plt.imshow(img)
else:
raise Exception("Unknown colour type")
plt.axis('off')
plt.title(title)
plt.show()
img = cv2.imread('s1.png')
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
ShowImage('Brain MRI',gray,'gray')
ret, thresh = cv2.threshold(gray,0,255,cv2.THRESH_OTSU)
ShowImage('Thresholding image',thresh,'gray')
ret, markers = cv2.connectedComponents(thresh)
#Get the area taken by each component. Ignore label 0 since this is the background.
marker_area = [np.sum(markers==m) for m in range(np.max(markers)) if m!=0]
#Get label of largest component by area
largest_component = np.argmax(marker_area)+1 #Add 1 since we dropped zero above
#Get pixels which correspond to the brain
brain_mask = markers==largest_component
brain_out = img.copy()
#In a copy of the original image, clear those pixels that don't correspond to the brain
brain_out[brain_mask==False] = (0,0,0)
img = cv2.imread('s1.png')
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(gray,0,255,cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)
# noise removal
kernel = np.ones((3,3),np.uint8)
opening = cv2.morphologyEx(thresh,cv2.MORPH_OPEN,kernel, iterations = 2)
# sure background area
sure_bg = cv2.dilate(opening,kernel,iterations=3)
# Finding sure foreground area
dist_transform = cv2.distanceTransform(opening,cv2.DIST_L2,5)
ret, sure_fg = cv2.threshold(dist_transform,0.7*dist_transform.max(),255,0)
# Finding unknown region
sure_fg = np.uint8(sure_fg)
unknown = cv2.subtract(sure_bg,sure_fg)
# Marker labelling
ret, markers = cv2.connectedComponents(sure_fg)
# Add one to all labels so that sure background is not 0, but 1
markers = markers+1
# Now, mark the region of unknown with zero
markers[unknown==255] = 0
markers = cv2.watershed(img,markers)
img[markers == -1] = [255,0,0]
im1 = cv2.cvtColor(img,cv2.COLOR_HSV2RGB)
ShowImage('Watershed segmented image',im1,'gray')
brain_mask = np.uint8(brain_mask)
kernel = np.ones((8,8),np.uint8)
closing = cv2.morphologyEx(brain_mask, cv2.MORPH_CLOSE, kernel)
ShowImage('Closing', closing, 'gray')
brain_out = img.copy()
#In a copy of the original image, clear those pixels that don't correspond to the brain
brain_out[closing==False] = (0,0,0)`
the picture shown below is a sample of what i want to get.this is what i get when running the code this is the original image
