Analysis of heuristic-based multilevel thresholding methods for image segmentation using R programming

The conventional way in analysing image segmentation algorithms manually is difficult since it requires a lot of human effort in keeping all data for analysis. Various heuristic algorithms are bundled with Otsu's and Kapur's objective function in finding optimal fitness and quality segmentation. In this work Otsu's and Kapur's objective function are bundled with heuristics such as harmony search optimisation (HSO) and electro magnetic optimisation (EMO) to compare the solution accuracy of segmented images In order to statistically analyse such algorithms, an automated tool is developed which takes an input image of any image category under consideration and extracts the segmented fitness values and quality parameters of the image. The extracted values are stored in a central database server constrained with image type, image category, methodology and heuristic used, no of thresholds and quality parameters. The central repository information is fed into data mining and data analytic tools to statistically rank the segmentation algorithms.