The An Adaptive Hybrid Clustering Framework for High-Precision Microarray Image Segmentation Using GA and BEMD

Hybrid Clustering Framework

Abstract

The development of microarray technology has facilitated expression profiling analysis for various medical and agricultural research areas. Despite the improving range of applications, the precision in isolating microarray images remains a challenge due to noise and variances in spot morphology. This research proposes a hybrid and adaptive clustering solution that offers great improvement in terms of accuracy, segmentation, noise reduction, processing time, and overall efficiency. We use an adaptive K-means clustering approach enhanced with Genetic Algorithms (GA) and Bi-Dimensional Empirical Mode Decomposition (BEMD). The average segmentation accuracy tested by us was close to 95% with our method whereas the K-means methods averaged at 85%, showing how vastly superior our method is. Our methods with the added BEMD and GA achieved unparalleled cutting down noise by 80% and increasing SNR 200%, reducing overall efficiency. Through optimising the algorithm’s performance, our algorithms resulted in an average of 1.2 seconds for image processing time which showcases the efficiency of the algorithm. These results enable uniquely resolving complex problems in microarray image analysis, unlocking new solutions critical for gene profiling medicine and agriculture, enabling transformative advancements in the sectors.