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copied!<p>There are a few things you can do to improve your results. To reduce noise in the image, you can apply a median blur before applying the Canny operator. This is a common de-noising technique. Also, try to avoid using the C API and <code>IplImage</code>.</p> <pre><code> cv::Mat img = cv::imread("c:\\dot1.bmp", 0); // dot1.bmp = Fig. 1 cv::medianBlur(img, img, 7); // Perform canny edge detection cv::Canny(img, img, 33, 100); </code></pre> <p>This significantly reduces the amount of noise in your edge image: <img src="https://i.stack.imgur.com/ZYYsQ.png" alt="Canny result"></p> <p>To better retain the original sizes of your dots, you can perform a few iterations of morphological closing with a smaller kernel rather than dilation. This will also reduce joining of the dots with the circle:</p> <pre><code>// This replaces the call to dilate() cv::morphologyEx(src, dilation, MORPH_CLOSE, cv::noArray(),cv::Point(-1,-1),2); </code></pre> <p>This will perform two iterations with a 3x3 kernel, indicated by using <code>cv::noArray()</code>.</p> <p>The result is cleaner, and the dots are completely filled:</p> <p><img src="https://i.stack.imgur.com/IOuyg.png" alt="Closing result"></p> <p>Leaving the rest of your pipeline unmodified gives the final result. There are still a few spurious mass centers from the circle, but considerably fewer than the original method:</p> <p><img src="https://i.stack.imgur.com/ZPCZX.png" alt="Mass centers"></p> <p>If you wanted to attempt removing the circle from the results entirely, you could try using <a href="http://docs.opencv.org/modules/imgproc/doc/feature_detection.html?#houghcircles" rel="noreferrer"><code>cv::HoughCircles()</code></a> and adjusting the parameters until you get a good result. This might have some difficulties because the entire circle is not visible in the image, only segments, but I recommend you experiment with it. If you did detect the innermost circle, you could use it as a mask to filter out external mass centers.</p>

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