平滑二进制图像的边缘

Question

如何平滑阈值化后获得的血管二元图像的边缘.

我尝试了一种类似于这种方法的方法,但没有得到我预期的结果.

这是代码:

import cv2
import numpy as np

INPUT = cv2.imread('so-br-in.png',0)
MASK = np.array(INPUT/255.0, dtype='float32')

MASK = cv2.GaussianBlur(MASK, (5,5), 11)
BG = np.ones([INPUT.shape[0], INPUT.shape[1], 1], dtype='uint8')*255

OUT_F = np.ones([INPUT.shape[0], INPUT.shape[1], 1],dtype='uint8')

for r in range(INPUT.shape[0]):
    for c in range(INPUT.shape[1]):
        OUT_F[r][c]  = int(BG[r][c]*(MASK[r][c]) + INPUT[r][c]*(1-MASK[r][c]))

cv2.imwrite('brain-out.png', OUT_F)  

可以做些什么来改善这些粗糙边缘的平滑度？

编辑

我想像http://pscs5.tumblr.com/post/60284570543那样平滑边缘.如何在OpenCV中执行此操作？

Answer 1

以下是我使用您的图片获得的结果:

我的方法主要基于cv::medianBlur应用于放大图像的几种方法.

这是代码:

cv::Mat vesselImage = cv::imread(filename); //the original image
cv::threshold(vesselImage, vesselImage, 125, 255, THRESH_BINARY);
cv::Mat blurredImage; //output of the algorithm
cv::pyrUp(vesselImage, blurredImage);

for (int i = 0; i < 15; i++)
    cv::medianBlur(blurredImage, blurredImage, 7);

cv::pyrDown(blurredImage, blurredImage);
cv::threshold(blurredImage, blurredImage, 200, 255, THRESH_BINARY);

锯齿状边缘是由于阈值.如果您对非二进制的输出图像感到满意(即具有256级grAy),则可以将其删除并获得此图像:

Answer 2

您可以扩张然后侵蚀区域http://docs.opencv.org/2.4/doc/tutorials/imgproc/erosion_dilation/erosion_dilation.html。

import cv2
import numpy as np
blur=((3,3),1)
erode_=(5,5)
dilate_=(3, 3)
cv2.imwrite('imgBool_erode_dilated_blured.png',cv2.dilate(cv2.erode(cv2.GaussianBlur(cv2.imread('so-br-in.png',0)/255, blur[0], blur[1]), np.ones(erode_)), np.ones(dilate_))*255)  

在这些东西之前编辑带有 4 的比例因子

Answer 3

我对@dhanushka对另一个问题的回答做了一些修改，并得到了这些图片。

抱歉，它是 C++ 代码，但也许您会将其转换为 Python。

您可以更改以下参数以获得不同的结果。

// contour smoothing parameters for gaussian filter
int filterRadius = 10; // you can try to change this value
int filterSize = 2 * filterRadius + 1;
double sigma = 20; // you can try to change this value

#include <opencv2/highgui.hpp>
#include <opencv2/imgproc.hpp>
#include <iostream>

using namespace cv;
using namespace std;

int main( int argc, const char** argv )
{
    Mat im = imread(argv[1], 0);

    Mat cont = ~im;
    Mat original = Mat::zeros(im.rows, im.cols, CV_8UC3);
    Mat smoothed = Mat(im.rows, im.cols, CV_8UC3, Scalar(255,255,255));

    // contour smoothing parameters for gaussian filter
    int filterRadius = 5;
    int filterSize = 2 * filterRadius + 1;
    double sigma = 10;

    vector<vector<Point> > contours;
    vector<Vec4i> hierarchy;
    // find contours and store all contour points
    findContours(cont, contours, hierarchy, CV_RETR_CCOMP, CV_CHAIN_APPROX_NONE, Point(0, 0));
    for(size_t j = 0; j < contours.size(); j++)
    {
        // extract x and y coordinates of points. we'll consider these as 1-D signals
        // add circular padding to 1-D signals
        size_t len = contours[j].size() + 2 * filterRadius;
        size_t idx = (contours[j].size() - filterRadius);
        vector<float> x, y;
        for (size_t i = 0; i < len; i++)
        {
            x.push_back(contours[j][(idx + i) % contours[j].size()].x);
            y.push_back(contours[j][(idx + i) % contours[j].size()].y);
        }
        // filter 1-D signals
        vector<float> xFilt, yFilt;
        GaussianBlur(x, xFilt, Size(filterSize, filterSize), sigma, sigma);
        GaussianBlur(y, yFilt, Size(filterSize, filterSize), sigma, sigma);
        // build smoothed contour
        vector<vector<Point> > smoothContours;
        vector<Point> smooth;
        for (size_t i = filterRadius; i < contours[j].size() + filterRadius; i++)
        {
            smooth.push_back(Point(xFilt[i], yFilt[i]));
        }
        smoothContours.push_back(smooth);

        Scalar color;

        if(hierarchy[j][3] < 0 )
        {
            color = Scalar(0,0,0);
        }
        else
        {
            color = Scalar(255,255,255);
        }
        drawContours(smoothed, smoothContours, 0, color, -1);
    }
    imshow( "result", smoothed );
    waitKey(0);
}