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Edge detection is the operation of finding the boundaries of objects present in an image. Classical methods use the image gradient or approximations of the image gradient to detect edge location. If you have a noisy image it is a good practice to reduce the noise before detecting the edges.

This is because noise might lead to false steps during edge detection, specially when gradient based methods are employed. Noise may produce unreliable oscillating derivative values across short distances. Let's investigate the profile of rows of our HELA image its red channel: Experiment the edge detection methods provided by the edge function in the smoothed image G.

Which one s works best? Try also after a Gaussian filter. Use the returned value for the gradient threshold to help you calibrate your edge detection. Kmeans is an iterative clustering technique that separates a data set into K mutually exclusive clusters, such that members within a cluster are closer to each other and to the cluster centroid its mean than to members and centroid of any other cluster.

When applied to perform image segmentation, Kmeans partitions the image into regions of similar intensities. It works very well for images with close to homogeneous regions. In MATLAB, use the function kmeans note that kmeans is not part of the image processing toolbox as it can be used for general data sets; it is a function of the statistics toolbox: We will need to reshape the image matrix to a format acceptable by kmeans a flat array: As it is, the kmeans segmentation seems to be a bit inferior when compared to the threshold segmentation we achieved in the previous lecture for the HELA nuclei image.

But we can easily adjust the kmeans result using morphological operations try also with imfill to fill holes:. Let's use a Gaussian filter: Edge contour detection Edge detection is the operation of finding the boundaries of objects present in an image. K-Means Kmeans is an iterative clustering technique that separates a data set into K mutually exclusive clusters, such that members within a cluster are closer to each other and to the cluster centroid its mean than to members and centroid of any other cluster.

But we can easily adjust the kmeans result using morphological operations try also with imfill to fill holes: Practice 1 - Experiment with the Kmeans demo for color image segmentation available in the image processing toolbox, "Color-Based Segmentation Using K-Means Clustering". Use the HELA image and try clustering in 2 regions, red and green only, since the blue channel is not very expressive; 2 - Write a script to do segmentation using the k-means procedure above.

Use as parameters things like filter type and its kernel size, what to use for 'Replicates' and 'start', and other values that might influence the k-means results.

Repeat the kmeans segmentation above for distinct values for 'Replicates' and see if you notice any difference.