Algorithm 1. Parallel Processing

1. Input image f(𝑥, 𝑦) 2. Compute the histogram h(i), i = 0, …., 255 for f(𝑥, 𝑦) 3. Parfor t = 0: 255 do 4. Compute ${\mathsf{\mu }}_{o Log}$(t) using Equation (20) 5. Compute ${\mathsf{\mu }}_{b Log}$(t) using Equation (21) 6. Compute ${\mathsf{\mu }}_{T Log}$(t) using Equation (22) 7. Compute ${\sigma }_{Lognormal}^2\left(t\right)$ using Equation (23). 8. Compute the original and the modified ${\sigma }_{Otsu}^2\left(t\right)$ based on their distributions. 9. Find the optimal t* which maximizes each of ${\sigma }_{Lognormal}^2\left(t\right)$ and the relevant ${\sigma }_{Otsu}^2\left(t\right)$ 10. End for. 11. Compute the average sum of the performance measure for each t*. 12. Find the best t* which maximizes the performance measure. 13. Return the best t* 14. Output image g(𝑥, 𝑦).