Electronics and Communications Engineering Department, Kakatiya Institute of Science and Technology Warangal, Warangal, Telangana, 506015, India.
Electrical Engineering Department, National Institute of Technology Warangal, Hanamkonda, Telangana, 506004, India.
Sci Rep. 2024 Sep 18;14(1):21833. doi: 10.1038/s41598-024-71448-6.
Multilevel thresholding image segmentation will subdivide an image into several meaningful regions or objects, which makes the image more informative and easier to analyze. Optimal multilevel thresholding approaches are extensively used for segmentation because they are easy to implement and offer low computational cost. Multilevel thresholding image segmentation is frequently performed using popular methods such as Otsu's between-class variance and Kapur's entropy. Numerous researchers have used evolutionary algorithms to identify the best multilevel thresholds based on the above approaches using histogram. This paper uses the Energy Curve (EC) based thresholding method instead of the histogram. Chaotic Bidirectional Smell Agent Optimization with Adaptive Control Strategy (ChBSAOACS), a powerful evolutionary algorithm, is developed and employed in this paper to create and execute an effective method for multilevel thresholding segmentation of breast thermogram images based on energy curves. The proposed algorithm was tested for viability on standard breast thermogram images. All experimental data are examined quantitatively and qualitatively to verify the suggested method's efficacy.
多阈值图像分割将图像细分为若干有意义的区域或对象，从而使图像更具信息量，更易于分析。最优多阈值方法广泛应用于分割，因为它们易于实现，计算成本低。多阈值图像分割通常使用 Otsu 的类间方差和 Kapur 的熵等流行方法来完成。许多研究人员使用进化算法根据上述方法使用直方图来确定最佳多阈值。本文使用基于能量曲线（EC）的阈值方法代替直方图。本文开发并使用了一种强大的进化算法——混沌双向嗅觉剂优化与自适应控制策略（ChBSAOACS），来创建和执行一种基于能量曲线的乳腺热图图像多阈值分割的有效方法。该算法在标准乳腺热图图像上进行了可行性测试。所有实验数据都进行了定量和定性分析，以验证所提出方法的有效性。
