tumor_extraction.py

import cv2
from processing import *

image = cv2.imread("mri.png", 1)
show_image('Original image', image)

#Step one - grayscale the image
grayscale_img = cvt_image_colorspace(image)
show_image('Grayscaled image', grayscale_img)

#Step two - filter out image
median_filtered = median_filtering(grayscale_img,5)
show_image('Median filtered', median_filtered)


#testing threshold function
bin_image = apply_threshold(median_filtered,  **{"threshold" : 160,
                                                 "pixel_value" : 255,
                                                 "threshold_method" : cv2.THRESH_BINARY})
otsu_image = apply_threshold(median_filtered, **{"threshold" : 0,
                                                 "pixel_value" : 255,
                                                 "threshold_method" : cv2.THRESH_BINARY + cv2.THRESH_OTSU})




'''
th2 = cv2.adaptiveThreshold(median_filtered,255,cv2.ADAPTIVE_THRESH_MEAN_C,\
            cv2.THRESH_BINARY,11,2)
th3 = cv2.adaptiveThreshold(median_filtered,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C,\
            cv2.THRESH_BINARY,11,2)
show_image('Image after adaptive thresholding', th2)
show_image('Image after adaptive thresholding 2', th3)

'''


#Step 3a - apply Sobel filter
img_sobelx = sobel_filter(median_filtered, 1, 0)
img_sobely = sobel_filter(median_filtered, 0, 1)

# Adding mask to the image
img_sobel = img_sobelx + img_sobely+grayscale_img
show_image('Sobel filter applied', img_sobel)

#Step 4 - apply threshold
# Set threshold and maxValue
threshold = 160
maxValue = 255

# Threshold the pixel values
thresh = apply_threshold(img_sobel,  **{"threshold" : 160,
                                                 "pixel_value" : 255,
                                                 "threshold_method" : cv2.THRESH_BINARY})
show_image("Thresholded", thresh)


#Step 3b - apply erosion + dilation
#apply erosion and dilation to show only the part of the image having more intensity - tumor region
#that we want to extract
kernel=cv2.getStructuringElement(cv2.MORPH_RECT,(9,9))
erosion = cv2.morphologyEx(median_filtered, cv2.MORPH_ERODE, kernel)
show_image('Eroded image', erosion)



dilation = cv2.morphologyEx(erosion, cv2.MORPH_DILATE, kernel)
show_image('Dilatated image', dilation)

#Step 4 - apply thresholding
threshold = 160
maxValue = 255

# apply thresholding
new_thresholding = apply_threshold(dilation,  **{"threshold" : 160,
                                                 "pixel_value" : 255,
                                                 "threshold_method" : cv2.THRESH_BINARY})
show_image('Threshold image after erosion + dilation', new_thresholding)