Fast Adaptive Entropy-driven Multilevel Image Segmentation using Look-up Tables on GPU Platforms

Georgi Petrov
Department of Telecommunications,
New Bulgarian University,
Sofia (Bulgaria)

https://doi.org/10.53656/math2026-1-2-fae

Abstract. This paper proposes a fast, adaptive method for multilevel image segmentation based on Kapur’s entropy. Implemented on CPU and GPU, the method achieves up to 40× acceleration using CUDA and memory optimization. Segmentation quality is preserved while enabling real-time processing of large image batches. The experiments were carried out using the university’s high-performance computing (HPC) mini-cluster, demonstrating its role as an educational and research platform. The approach is scalable and suited for scientific and educational use. It builds upon earlier work on multidimensional histogram analysis, applying entropy-driven modeling to image segmentation.
Keywords: entropy, multilevel thresholding, GPU, LUT

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