A novel evolutionary row class entropy based optimal multi-level thresholding technique for brain MR images

The local averaging technique adopted for the construction of 2D histogram in Otsu's method fails to preserve the edge information. Further, the consideration of the diagonal pixels only results in the loss of information. These make the 2D Otsu method of multi-level thresholding inefficient to retain the spatial correlation information. Although the computation of 2D histogram based on gray gradient information is a better way to threshold an image, it faces a backlash due to the high magnitude peaks. To solve these problems, we suggest a new normalized local variance (NLV) method for constructing 2D histogram using the local variance followed by a novel evolutionary row class entropy (ERCE) method for optimal multi-level image thresholding, which tries to preserve maximum spatial information through normalization of the local variance. A new optimization technique called hybrid Adaptive Cuckoo Search-Squirrel Search Algorithm (ACS-SSA) is also introduced. A new fitness function is suggested. The standard CEC 2005 benchmark test functions are used to validate the performance of our proposed ACS-SSA technique. The optimum threshold values obtained are used to segment 100 slices of T-2 weighted axial brain MR images (taken from the Harvard Medical School database). Several performance evaluation metrics are computed to compare the performance of our method with the state-of-the-art methods. The analysis of the results shows that ERCE method outperforms other methods. This method may set a new direction in the multilevel image thresholding research.

Automated summary

The paper proposes a new normalized local variance (NLV) method for constructing 2D histogram, followed by a novel evolutionary row class entropy (ERCE) method for optimal multi-level image thresholding, which aims to preserve maximum spatial information through normalization of the local variance.