OpenCV Segmentation of Largest contour in Breast Mammograms

问题

This might be a bit too "general" question, but how do I perform GRAYSCALE image segmentation and keep the largest contour? I am trying to remove background noise (i.e. labels) from breast mammograms, but I am not successful. Here is the original image:

First, I applied AGCWD algorithm (based on paper "Efficient Contrast Enhancement Using Adaptive Gamma Correction With Weighting Distribution") in order to get better contrast of the image pixels, like so:

Afterwards, I tried executing following steps:

Image segmentation using OpenCV's KMeans clustering algorithm:

enhanced_image_cpy = enhanced_image.copy()
reshaped_image = np.float32(enhanced_image_cpy.reshape(-1, 1))

number_of_clusters = 10
stop_criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 100, 0.1)

ret, labels, clusters = cv2.kmeans(reshaped_image, number_of_clusters, None, stop_criteria, 10, cv2.KMEANS_RANDOM_CENTERS)
clusters = np.uint8(clusters)

Canny Edge Detection:

removed_cluster = 1

canny_image = np.copy(enhanced_image_cpy).reshape((-1, 1))
canny_image[labels.flatten() == removed_cluster] = [0]

canny_image = cv2.Canny(canny_image,100,200).reshape(enhanced_image_cpy.shape)
show_images([canny_image])

Find and Draw Contours:

initial_contours_image = np.copy(canny_image)
initial_contours_image_bgr = cv2.cvtColor(initial_contours_image, cv2.COLOR_GRAY2BGR)
_, thresh = cv2.threshold(initial_contours_image, 50, 255, 0)
contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
cv2.drawContours(initial_contours_image_bgr, contours, -1, (255,0,0), cv2.CHAIN_APPROX_SIMPLE)
show_images([initial_contours_image_bgr])

Here is how image looks after I draw 44004 contours:

I am not sure how can I get one BIG contour, instead of 44004 small ones. Any ideas how to fix my approach, or possibly any ideas on using alternative approach to get rid of label in top right corner.

Thanks in advance!

回答1:

Here is one way to do that in Python OpenCV

Read the image

Threshold and invert so the borders are black
Remove the borders of the image as follows (so as to make it easier to get the relevant contours later):
Count the number of non-zero pixels in each column and find the first and last column that have counts greater than 0
Count the number of non-zero pixels in each row and find the first and last row that have counts greater than 0
Crop the image to remove the borders
Crop thresh1 and invert to make thresh2
Get the external contours from thresh2
Find the largest contour and draw as white filled on a black background as a mask
Make all pixels in the cropped image black where the mask is black
Save the results -

Input:

import cv2
import numpy as np

# read image
img = cv2.imread('xray3.png')

# convert to gray
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# threshold and invert
thresh1 = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY)[1]
thresh1 = 255 - thresh1

# remove borders
# count number of white pixels in columns as new 1D array
count_cols = np.count_nonzero(thresh1, axis=0)

# get first and last x coordinate where black
first_x = np.where(count_cols>0)[0][0]
last_x = np.where(count_cols>0)[0][-1]
print(first_x,last_x)

# count number of white pixels in rows as new 1D array
count_rows = np.count_nonzero(thresh1, axis=1)

# get first and last y coordinate where black
first_y = np.where(count_rows>0)[0][0]
last_y = np.where(count_rows>0)[0][-1]
print(first_y,last_y)

# crop image
crop = img[first_y:last_y+1, first_x:last_x+1]

# crop thresh1 and invert
thresh2 = thresh1[first_y:last_y+1, first_x:last_x+1]
thresh2 = 255 - thresh2

# get external contours and keep largest one
contours = cv2.findContours(thresh2, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
contours = contours[0] if len(contours) == 2 else contours[1]
big_contour = max(contours, key=cv2.contourArea)

# make mask from contour
mask = np.zeros_like(thresh2 , dtype=np.uint8)
cv2.drawContours(mask, [big_contour], 0, 255, -1)

# make crop black everywhere except where largest contour is white in mask
result = crop.copy()
result[mask==0] = (0,0,0)

# write result to disk
cv2.imwrite("xray3_thresh1.jpg", thresh1)
cv2.imwrite("xray3_crop.jpg", crop)
cv2.imwrite("xray3_thresh2.jpg", thresh2)
cv2.imwrite("xray3_mask.jpg", mask)
cv2.imwrite("xray3_result.png", result)

# display it
cv2.imshow("thresh1", thresh1)
cv2.imshow("crop", crop)
cv2.imshow("thresh2", thresh2)
cv2.imshow("mask", mask)
cv2.imshow("result", result)
cv2.waitKey(0)

Threshold 1 image:

Cropped image:

Threshold 2 image:

Mask image:

Result:

来源：https://stackoverflow.com/questions/64850305/opencv-segmentation-of-largest-contour-in-breast-mammograms

标签

python