Visual C＋＋6.0是由 Microsoft 公司推出的一款面向对象的计算机程序开发工具，是编程入门的良好编译工具，在 Windows 环境下很常用，是使用最广的开发工具。AOS 是高级在轨系统（Advanced Orbiting Systems）的缩略词，主要用来达成...Visual C＋＋6.0是由 Microsoft 公司推出的一款面向对象的计算机程序开发工具，是编程入门的良好编译工具，在 Windows 环境下很常用，是使用最广的开发工具。AOS 是高级在轨系统（Advanced Orbiting Systems）的缩略词，主要用来达成航天器与地面站之间的双向传送。本文中应用 Visual C＋＋6.0程序设计软件，根据 AOS 空间包提取的方法，给出实验仿真结果。展开更多
In order to develop an automated segmentation system for Computed Tomography （CT） brain images, a new approach which consists of several unsupervised segmcotation techniques was introduced. The system segments the C...In order to develop an automated segmentation system for Computed Tomography （CT） brain images, a new approach which consists of several unsupervised segmcotation techniques was introduced. The system segments the CT brain images into three partitions, i. e., abnormalities, cerebrospinal fluid （CSF）, and brain matter. Our approach consists of two phase-segmentation methods. In the first phase segmentation, k-means and fuzzy cmeans （FCM） methods were implemented to segment and transform the images into the binary images. Based on the connected component in binary images, a decision tree was employed for the annotation of normal or abnormal regions. In the second phase segmentation, the modified FCM with population-diameter independent （PDI） segmentation was applied to segment the images into CSF and brain matter. The experimental results have shown that our proposed system is feasible and yield satisfactory results.展开更多
文摘Visual C＋＋6.0是由 Microsoft 公司推出的一款面向对象的计算机程序开发工具，是编程入门的良好编译工具，在 Windows 环境下很常用，是使用最广的开发工具。AOS 是高级在轨系统（Advanced Orbiting Systems）的缩略词，主要用来达成航天器与地面站之间的双向传送。本文中应用 Visual C＋＋6.0程序设计软件，根据 AOS 空间包提取的方法，给出实验仿真结果。
文摘In order to develop an automated segmentation system for Computed Tomography （CT） brain images, a new approach which consists of several unsupervised segmcotation techniques was introduced. The system segments the CT brain images into three partitions, i. e., abnormalities, cerebrospinal fluid （CSF）, and brain matter. Our approach consists of two phase-segmentation methods. In the first phase segmentation, k-means and fuzzy cmeans （FCM） methods were implemented to segment and transform the images into the binary images. Based on the connected component in binary images, a decision tree was employed for the annotation of normal or abnormal regions. In the second phase segmentation, the modified FCM with population-diameter independent （PDI） segmentation was applied to segment the images into CSF and brain matter. The experimental results have shown that our proposed system is feasible and yield satisfactory results.
