Daniel Ebenezer, Anitha J
Department of Electronics and Communication Engineering, Karunya University, Coimbatore 641114, India.
Comput Biol Med. 2016 Apr 1;71:149-55. doi: 10.1016/j.compbiomed.2016.02.011. Epub 2016 Feb 26.
Unsharp masking techniques are a prominent approach in contrast enhancement. Generalized masking formulation has static scale value selection, which limits the gain of contrast. In this paper, we propose an Optimum Wavelet Based Masking (OWBM) using Enhanced Cuckoo Search Algorithm (ECSA) for the contrast improvement of medical images. The ECSA can automatically adjust the ratio of nest rebuilding, using genetic operators such as adaptive crossover and mutation. First, the proposed contrast enhancement approach is validated quantitatively using Brain Web and MIAS database images. Later, the conventional nest rebuilding of cuckoo search optimization is modified using Adaptive Rebuilding of Worst Nests (ARWN). Experimental results are analyzed using various performance matrices, and our OWBM shows improved results as compared with other reported literature.
非锐化掩膜技术是对比度增强中的一种突出方法。广义掩膜公式具有固定的尺度值选择，这限制了对比度增益。在本文中，我们提出了一种基于最优小波的掩膜（OWBM），使用增强型布谷鸟搜索算法（ECSA）来改善医学图像的对比度。ECSA可以使用自适应交叉和变异等遗传算子自动调整巢穴重建的比例。首先，使用Brain Web和MIAS数据库图像对所提出的对比度增强方法进行定量验证。随后，使用最差巢穴的自适应重建（ARWN）对布谷鸟搜索优化的传统巢穴重建进行修改。使用各种性能指标对实验结果进行分析，与其他已发表文献相比，我们的OWBM显示出更好的结果。
