Segmentation Of Neutrophil Nucleus in White Blood Cell Images Using Kapur’s entropy based Improved Grey Wolf Optimization

Qualitative analysis of blood disorders is the most challenging job in current medical image. Diagnosing disorders through qualitative analysis relies heavily on approximation. Disorders related to white blood cells (WBC) are notably prevalent in medical practice. Identifying blood disorders leads to classification of various blood related conditions. Hence, the automated identification of blood related disorder allows to bypass the existing complex environment and focus on the complex image insight provided by the images. In this study, the automated segmentation of neutrophil nucleus in white blood cells microscopic through Kapur’s entropy multilevel thresholding technique. In the process of an efficient segmentation through Kapur’s entropy, the threshold optimal values are required to change the class variance or entropy criteria. To achieve these optimizations, Improved Grey Wolf (IGWO) nature-inspired metaheuristic algorithms are employed for this study. The experimental test has conducted on standard images using various levels of threshold (2, 3, 4, 5 and 6). The efficacy of Kapur’s entropy based improved grey wolf optimization approach has been evaluated and performed with established optimization methods contains Genetic Algorithm(GA), Moth Fly Optimization (MFO), Firefly Optimization, Fire Hawk Optimization and Particle Swarm Optimization (PSO). Results demonstrated that Kapur’s entropy based IGWO outperformed for neutrophil nucleus segmentation in microscopic images when compared to other optimization techniques in both quality and consistency.