![]() Some enhancements in the edges using OpenCV (image source author)Īs you can see, the edges are now complete and much smoother than before. Typically there are four maps, all of which depict a certain feature and are analyzed together for diagnosis (further details are out of current scope). It is a typical report generated by medical instruments used in the field of Neurological Science which use sensors to detect signals from a patients brain and display them as colored maps. So let us start by looking at the input image itself. in a Machine Learning model which can diagnose any health anomalies. The extracted segments can then be used in numerous applications e.g. Our task today is to extract the desired segments from an image which contains a snapshot of a patients brain activity map. In this post we will look at a somewhat more complex problem and explore some methods which we can use to obtain the desired results. We used simple OpenCV functions like inRange, findContours, boundingRect, minAreaRect, minEnclosingCircle, circle, HoughLines, line etc to achieve our objective.įor beginners in OpenCV, I would recommend to go through that post in order to get familiar with the usage of the above functions. Welcome to the second post in this series where we talk about extracting regions of interest (ROI) from images using OpenCV and Python.Īs a recap, in the first post of this series we went through the steps to extract balls and table edges from an image of a pool table. Using OpenCV for efficiently extracting ROI from images
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