Research Article
Improving Minimum Cross-Entropy Thresholding for Segmentation of Infected Foregrounds in Medical Images Based on Mean Filters Approaches
Algorithm 2
MCET using different mean estimation approaches.
| (1) | Read input image (, ) | | (2) | Compute the histogram h(i), i = 0, …., 255 for (, ) | | (3) | for t = 2 : 254 do | | (4) | Compute the n(t) in equation (4) based on: | | | Compute and using classical mean | | | Compute and using lognormal mean | | | Compute and using harmonic mean | | | Compute and using geometric mean | | | Compute and using contraharmonic mean | | | Apply different input of Q (see Tables 1 and 2) | | | Compute and using an alpha-trimmed mean | | | Apply different input of trim (see Tables 1 and 2) | | (5) | Find the optimal that minimize n(t) for each mean estimation input | | (6) | End for | | (7) | Compute the average sum of the performance measure for each | | (8) | Find the best that maximizes the performance measure | | (9) | Return the best | | (10) | Output image |
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