How to remove horizontal line from this x-ray image? - c++

Question

I'm new in opencv (c++) and I want to remove horizontal line from this x-ray image. But I can not.
These is my image:
What ideas on how to solve this task would you suggest? Or on what resource on the internet can I find help?
This is my c++ code
src = imread("C:/Users/Alireza/Desktop/New folder (3)/11.bmp");
cvtColor(src, gray, CV_RGB2GRAY);
imshow("Original Image", gray);
imwrite("Original Image.png", gray);
normalize(gray, gray, 0, 250, NORM_MINMAX, -1, Mat());
threshold(gray, thresh, 170, 255, THRESH_BINARY_INV);
vector< vector <Point> > contours;
vector< Vec4i > hierarchy;
int largest_contour_index = 0;
int largest_area = 0;
Mat alpha(src.size(), CV_8UC1, Scalar(0));
findContours(thresh, contours, hierarchy, CV_RETR_TREE,CV_CHAIN_APPROX_SIMPLE, Point(0, 0));
for (int i = 0; i< contours.size(); i++)
{
double a = contourArea(contours[i], false);
if (a>largest_area)
{
largest_area = a;
largest_contour_index = i;
}
}
drawContours(alpha, contours, largest_contour_index,Scalar(255),CV_FILLED, 8, hierarchy);
vector<Mat> rgb;
split(src, rgb);
Mat rgba[4] = { rgb[0], rgb[1], rgb[2], alpha };
merge(rgba, 4, Tafrigh);
imshow("Tafrigh", Tafrigh);
imwrite("Tafrigh.png", Tafrigh);

Answer 1

Take a 2D FFT, take a look at the spectrum. You will see plenty of dots along the center y-axis. Suppress those dots , back transform, your vertical lines will be gone.
Below the result (since I don't have C++ and opencv installed) in Python, with sliders to vary the region to be suppressed. Consider it Pseudocode. This is still pretty rough as I'm not making a smooth transition between suppressed pixels and their neighbors here for simplicity.
%matplotlib inline
from __future__ import division
import numpy as np
import matplotlib.pyplot as p
from ipywidgets import *
from scipy import misc
f = misc.imread('xray_image_with_horizontal_lines.png')
a=np.fft.fftshift(np.fft.fft2(f))
def process(kx,ky):
p.figure(figsize=(12,8))
p.subplot(221)
p.imshow(f, cmap=p.cm.gray)
p.subplot(222)
p.imshow(np.abs(np.log(a)), cmap=p.cm.gray)
print np.shape(a)
b=np.zeros_like(a)
for i in range(639):
for j in range(406):
if not ( 320-kx<i<320+kx and (j<203-ky or j>203+ky)):
b[j,i]=a[j,i]
c=np.fft.ifft2(b)
p.subplot(223)
p.imshow(np.abs(np.log(b)), cmap=p.cm.gray)
p.subplot(224)
p.imshow(np.abs(c), cmap=p.cm.gray)
interact(process, kx=[1,20,1],ky=[1,20,1])

Answer 2

That's just an idea : keep mean line constant.
cv::Mat image = cv::imread("Tf6HO.png",CV_LOAD_IMAGE_GRAYSCALE);
vector<double> moyenne;
double minval,maxval;
minMaxLoc(image,&minval,&maxval);
imshow("original",image);
for (int i = 0; i < image.rows; i++)
{
double s=0;
// Caluclate mean for row i
for (int j=0;j<image.cols;j++)
s += image.at<uchar>(i,j);
// Store result in vector moyenne
moyenne.push_back(s/image.cols);
}
// Energy for row i equal to a weighted mean of row in [i-nbInf,i+nbSup]
int nbInf=32,nbSup=0;
for (int i = 0; i < image.rows; i++)
{
double s=0,p=0;
// weighted mean (border effect process with max and min method
for (int j = max(0, i - nbInf); j <= min(image.rows - 1, i + nbSup); j++)
{
s+=moyenne[j]*1./(1+abs(i-j));
p+=1./(1+abs(i-j));
}
// Weighted mean
s/=p;
// process pixel in row i : mean of row i equal to s
for (int j=0;j<image.cols;j++)
image.at<uchar>(i,j) =saturate_cast<uchar>((image.at<uchar>(i,j)-moyenne[i])+s);
}
imshow("process",image);
waitKey();
or with reduce resolution
if you want to improve you can read this paper and bibliography