An adaptive hybrid clustering framework for high-precision microarray image segmentation using GA and BEMD
The development of microarray technology has facilitated expression profiling analysis for various medical and agricultural research areas. Despite the increasing range of applications, 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 significant improvement in terms of accuracy, segmentation, noise reduction, processing time, and overall efficiency. The study used an adaptive K-means clustering approach enhanced with genetic algorithms and bi-dimensional empirical mode decomposition. This hybrid framework achieved an average segmentation accuracy of approximately 95%, compared to 85% with conventional K-means, showing its superiority. In addition, the enhanced method achieved unparalleled noise reduction by 80% and improved signal-to-noise ratio by 200%, while maintaining efficiency with an average image processing time of 1.2 s. These results uniquely address complex challenges in microarray image analysis, unlocking new solutions critical for gene profiling in medicine and agriculture, and driving transformative advancements in the sectors.
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