Smoothing Edges of a Binary Image

Here is the result I obtained with your image:

My method is mostly based on several cv::medianBlurapplied on a scaled-up image.

Here is the code:

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

The jagged edges are due to the thresholding. If you are comfortable with an output image that is non-binary (i.e. with 256 shades of grAy), you can just remove it and you get this image:

You can dilate then erode the areas http://docs.opencv.org/2.4/doc/tutorials/imgproc/erosion_dilatation/erosion_dilatation.html.

import cv2import numpy as npblur=((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)  

EDIT whith a scale facor off 4 before the stuff

i made some modifications on @dhanushka 's answer for another question and get these images.

Sorry it is C++ code but maybe you will convert it to Python.

You can change the parameters below to get different results.

// contour smoothing parameters for gaussian filterint filterRadius = 10; // you can try to change this valueint 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);}