Efficient superpixel-based brain MRI segmentation using multi-scale morphological gradient reconstruction and quantum clustering

Segmentation of brain MRI images is a fundamental task in medical image analysis. However, existing clustering methods often face significant challenges, including high computational complexity in calculating distances between cluster centers and pixels at each iteration, sensitivity to initial parameters and noise, and inadequate consideration of local spatial structures. This paper introduces an innovative method, Efficient Superpixel-Based Brain MRI Segmentation using Multi-Scale Morphological Gradient Reconstruction and Quantum Clustering, designed to address these challenges. The aim is to develop an efficient and robust segmentation technique that enhances accuracy while mitigating computational and parameter-related issues. To achieve this, we propose a multi-scale morphological gradient reconstruction operation that generates precise superpixel images, thereby improving the representation of local spatial features. These superpixel images are then used to compute histograms, effectively compressing the original color image data. Quantum clustering is subsequently applied to these superpixels using histogram parameters, leading to the desired segmentation outcomes. Experimental results demonstrate that our method outperforms state-of-the-art clustering techniques in terms of both segmentation accuracy and processing speed. These findings underscore the proposed approach's potential to overcome traditional methods' limitations, offering a promising solution for brain MRI segmentation in medical imaging.