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You can separate the histogram into 2 gaussians using expectation maximization for a gaussian mixture model. Search Google for examples.
Once you have the parameters of the 2 gaussians, you can compute their intersection point. Use this value as threshold for optimal separation of the two classes.
Another solution would be to use the K-means algorithm with 2 classes; it should estimate automatically the optimal threshold between the two classes. Try both methods to see which one works better in your case.
