CT Image Segmentation Using Optimization Techniques under Neutrosophic Domain

Doaa El-Shahat, Nourhan Talal, Jun Ye, Wenhua Cui
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Abstract

In this paper, we introduce a hybrid technique between optimization algorithms and neutrosophic theory. This new hybridization can deal with uncertainties in brain computed tomography (CT) images in three different memberships very effectively. To prove the real-time application of this theory, a new segmentation method for brain CT medical images is presented. The grayscale medical image suffers from uncertainties and inconsistencies in the gray levels due to their bad luminance. The proposed technique addressed this problem by performing neutrosophic operations on gray levels based on the S membership function.
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利用中性域下的优化技术进行 CT 图像分割
本文介绍了优化算法与中性理论的混合技术。这种新的混合技术可以非常有效地处理脑计算机断层扫描(CT)图像中三种不同成员的不确定性。为了证明这一理论的实时应用,本文提出了一种新的脑 CT 医学影像分割方法。灰度医学图像由于亮度不佳而存在灰度的不确定性和不一致性。所提出的技术根据 S 成员函数对灰度级进行中性运算,从而解决了这一问题。
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