Department of Mathematics and Computer Science, Faculty of Science, Beirut Arab University, Beirut, Lebanon.
Contrast Media Mol Imaging. 2022 Mar 17;2022:9289574. doi: 10.1155/2022/9289574. eCollection 2022.
Mean-based thresholding methods are among the most popular techniques that are used for images segmentation. Thresholding is a fundamental process for many applications since it provides a good degree of intensity separation of given images. Minimum cross-entropy thresholding (MCET) is one of the widely used mean-based methods for images segmentation; it is based on a classical mean that remains steady and limited value. In this paper, to improve the efficiency of MCET, dedicated mean estimation approaches are proposed to be used with MCET, instead of using the classical mean. The proposed mean estimation approaches, for example, alpha trim, harmonic, contraharmonic, and geometric, tend to exclude the negative impact of the undesired parts from the mean computation process, such as noises, local outliers, and gray intensity levels, and then provide an improvement for the thresholding process that can reflect good segmentation results. The proposed technique adds a profound impact on accurate images segmentation. It can be extended to other applications in object detection. Three data sets of medical images were applied for segmentation in this paper, including magnetic resonance imaging (MRI) Alzheimer's, MRI brain tumor, and skin lesion. The unsupervised and supervised evaluations were used to conduct the efficiency of the proposed method.
基于均值的阈值方法是最受欢迎的图像分割技术之一。阈值处理是许多应用程序的基本过程，因为它可以提供给定图像的良好强度分离程度。最小交叉熵阈值（MCET）是一种广泛用于图像分割的基于均值的方法，它基于一个稳定且有限的经典均值。在本文中，为了提高 MCET 的效率，提出了专门的均值估计方法来与 MCET 一起使用，而不是使用经典均值。提出的均值估计方法，例如 alpha trim、harmonic、contraharmonic 和 geometric，倾向于从均值计算过程中排除不期望部分的负面影响，例如噪声、局部异常值和灰度级，然后为可以反映良好分割结果的阈值处理提供改进。所提出的技术对准确的图像分割有深远的影响。它可以扩展到其他对象检测应用中。本文应用了三个医学图像数据集进行分割，包括磁共振成像（MRI）阿尔茨海默病、MRI 脑肿瘤和皮肤病变。本文采用无监督和监督评估来评估该方法的效率。
