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Image segmentation is an important task involved in different areas from image processing to image analysis. One of the simplest methods for image segmentation is thresholding. However, many thresholding methods are based on a bi-level thresholding procedure. These methods can be extended to form multi-level thresholding, but they become computationally expensive because a large number of iterations would be required for computing the optimum threshold values. In order to overcome this disadvantage, a new method based on a Shrinking Search Space (3S) algorithm is proposed in this paper. The method is applied on statistical bi-level thresholding approaches including Entropy, Cross-entropy, Covariance, and Divergent Based Thresholding (DBT), to achieve multi-level thresholding and used for intracranial segmentation from brain MRI images. The paper demonstrates that the impact of the proposed 3S technique on the DBT method is more significant than the other bi-level thresholding approaches. Comparing the results of using the proposed approach against those of the Fuzzy C-Means (FCM) clustering method demonstrates a better segmentation performance by improving the similarity index from 0.58 in FCM to 0.68 in the 3S method. Also, this method has a lower computation complexity of around 0.37s with respect to 157s processing time in FCM. In addition, the FCM approach does not always guarantee the convergence, whilst the 3S technique always converges to the optimum result.

A 3S Multi-level Thresholding Technique for Intracranial Segmentation from Brain MRI Images