Applying Ant Colony Optimization algorithms and variants for lung nodule detection

Ant Colony Optimization (ACO) algorithms are widely used in medical imaging, especially for image edge detection and image segmentation. In this paper, fundamentally we use ACO algorithm for lung nodule detection and compare the performance against three other algorithms namely Otsu algorithm, Watershed algorithm, Global region based segmentation. In addition, we suggest a novel approach which involves variations of ACO namely Refined ACO, where number of iterations are extended based on performance parameters, Logical ACO, where outputs of every refined iterations are XORed to be fed for subsequent iterations and Variant ACO, where the outputs of Otsu algorithm becomes the input of refined ACO.