Color Hippocampus Image Segmentation Using Quantum Inspired Firefly Algorithm and Merging of Channel-Wise Optimums

Color image segmentation is essential for medical image processing to figure out the cells, tissues, lesion areas, etc. The hippocampus is an extension of the temporal lobe of the brain. This area of the brain has been intensively studied for its clinical significance. It is the first and most severely affected structure in neuropsychiatric conditions. Meta-heuristic algorithm-based optimal segmentation is a widely accepted method in the medical domain. In this work, a hybrid method called the quantum-inspired firefly algorithm (QIFA) has been implemented in a multi-core environment to perform color segmentation of the hippocampus images in a parallel manner. The parallel QIFA runs on three different channels, Red, Green, and Blue of the input color image, and a subsequent merging is applied. The correlation has been considered as the objective function. Finally, a study has been carried out concerning various image segmentation evaluation parameters, and the proposed method has been compared to other metaheuristic algorithms. The analysis of the results shows that the method is effective for medical image segmentation. The speed-up of the technique has also been examined in detail for various image sizes and color levels.