Edge magnitude based multilevel thresholding using Cuckoo search technique

• Edge magnitudes from GLCM are used for multilevel thresholding.
• New objective functions are derived and proposed.
• A meta-heuristic search algorithm called Cuckoo search algorithm is used to optimize edge magnitudes.
• Optimum threshold values are obtained corresponding to the edge magnitudes.
• Results are compared with histogram based between class variance method for multilevel thresholding.

Multilevel thresholding technique is popular and extensively used in the field of image processing. In this paper, a multilevel threshold selection is proposed based on edge magnitude of an image. The gray level co-occurrence matrix (second order statistics) of the image is used for obtaining multilevel thresholds by optimizing the edge magnitude using Cuckoo search technique. New theoretical formulation for objective functions is introduced. Key to our success is to exploit the correlation among gray levels in an image for improved thresholding performance. Apart from qualitative improvements the method also provides us optimal threshold values. Results are compared with histogram (first order statistics) based between-class variance method for multilevel thresholding. It is observed that the results of our proposed method are encouraging both qualitatively and quantitatively.