Image transition region extraction and thresholding with nonlocal spatial feature

Because of the only use of local spatial features, classical methods for transition region extraction and thresholding would result in unsatisfied, even complete failure results under the existence of noises or outliers. In view of this, we propose a novel algorithm based on nonlocal spatial feature and gray level difference. This algorithm generates the nonlocal spatial feature and gray level difference first, and constructs the effective feature matrix based on the above two features, then obtains an automatic threshold related to the effective feature matrix according to a statistical method for thresholding, meanwhile extracts the transition region. Finally, the algorithm obtains the optimal grayscale threshold by calculating the grayscale mean of transition pixels, and yields the binary result. Experimental results show that, the proposed algorithm performs good result of transition region extraction and thresholding, and it is reasonable and effective, can be as an alternative to traditional methods.