Neurologist analyses shape and structure of brain parts through any medical images such as CT, MRI, and PET for disease diagnosis. For diagnosis, automatic medical image segmentation segments the parts of brain with low contrast, and artefacts are never removed over boundary region in different parts of brain. Manual segmentation shows poor differentiation in boundary regions due to artefacts or steaks. In this paper, we propose dyadic CAT optimisation (DCO) algorithm for segmenting the brain regions from CT and MRI images via nonlinear perspective foreground and background projection. DCO algorithm provides exact structure and shape of brain regions and eliminates artefacts in boundary regions. DCO algorithm delineates the boundary region such as dentate nucleus, pontine tegmentum, pontine nuclei, petrosal nerve, petrous part of temporal bone, crista galli, internal occipital crest, and mastoid emissary foramen in brain image with high visibility and enhanced boundary and differentiates deformable shape. Performance of DCO algorithm is evaluated through 50 MRI and CT brain images and eight images with complex bone and muscle mass structures of brain. DCO algorithm shows an accuracy of 90% through structural similarity index.

神经科医生通过 CT、MRI 和 PET 等任何医学图像分析大脑部位的形状和结构，以进行疾病诊断。对于诊断，自动医学图像分割将低对比度的大脑部分进行分割，并且在大脑不同部分的边界区域上永远不会去除伪影。由于人工制品或牛排，手动分割显示边界区域的差异很小。在本文中，我们提出了二元 CAT 优化 (DCO) 算法，用于通过非线性透视前景和背景投影从 CT 和 MRI 图像中分割大脑区域。DCO 算法提供大脑区域的精确结构和形状，并消除边界区域中的伪影。DCO算法对齿状核、脑桥被盖、脑桥核、岩神经、颞骨岩部、脑图像中的嵴、枕内嵴和乳突使者孔具有高可见度和增强的边界并区分可变形的形状。DCO 算法的性能通过 50 张 MRI 和 CT 脑图像和 8 幅具有复杂大脑骨骼和肌肉质量结构的图像进行评估。DCO算法通过结构相似度指标显示90%的准确率。