Meta-Heuristic Algorithms in Medical Image Segmentation

Abstract

Artificial intelligence is the outlet of computer science apprehensive with creating computers that perform as humans. It compromises expert systems, playing games, natural language, and robotics. However, soft computing (SC) varies from the hard (conventional) computing in its tolerant of partial truth, uncertainty, imprecision, and approximation, thus, it models the human mind. The most common SC techniques include neural networks, fuzzy systems, machine learning, and the meta-heuristic stochastic algorithms (e.g., Cellular automata, ant colony optimization, Memetic algorithms, particle swarms, Tabu search, evolutionary computation and simulated annealing. Due to the required accurate diseases analysis, magnetic resonance imaging, computed tomography images and images of other modalities segmentation remains a challenging problem. Over the past years, soft computing approaches attract attention of several researchers for problems solving in medical data applications. Image segmentation is the process that partitioned an image into some groups based on similarity measures. This process is employed for abnormalities volumetric analysis in medical images to identify the disease nature. Recently, meta-heuristic algorithms are conducted to support the segmentation techniques. In the current chapter, different segmentation procedures are addressed. Several meta-heuristic approaches are reported with highlights on their procedures. Finally, several medical applications using meta-heuristic based-approaches for segmentation are discussed.