Image segmentation is an important task involved in different areas from image processing to image analysis.\r\nOne of the simplest methods for image segmentation is thresholding. However, many thresholding methods are\r\nbased on a bi-level thresholding procedure. These methods can be extended to form multi-level thresholding, but they\r\nbecome computationally expensive because a large number of iterations would be required for computing the optimum\r\nthreshold values. In order to overcome this disadvantage, a new method based on a Shrinking Search Space (3S)\r\nalgorithm is proposed in this paper. The method is applied on statistical bi-level thresholding approaches including\r\nEntropy, Cross-entropy, Covariance, and Divergent Based Thresholding (DBT), to achieve multi-level thresholding and\r\nused for intracranial segmentation from brain MRI images. The paper demonstrates that the impact of the proposed\r\n3S technique on the DBT method is more significant than the other bi-level thresholding approaches. Comparing the\r\nresults of using the proposed approach against those of the Fuzzy C-Means (FCM) clustering method demonstrates\r\na better segmentation performance by improving the similarity index from 0.58 in FCM to 0.68 in the 3S method. Also,\r\nthis method has a lower computation complexity of around 0.37s with respect to 157s processing time in FCM. In\r\naddition, the FCM approach does not always guarantee the convergence, whilst the 3S technique always converges\r\nto the optimum res.
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