• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Intelligent automation and soft computing >MRI Brain Image Segmentation Using Enhanced Adaptive Fuzzy K-Means Algorithm
【24h】

MRI Brain Image Segmentation Using Enhanced Adaptive Fuzzy K-Means Algorithm

机译：使用增强型自适应模糊K均值算法的MRI脑图像分割

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Medical images are widely used to plan further treatment for the patient. However, the images sometimes are corrupted with a noise, which normally exists or occurs during storage or while transferring the image. Therefore, the need to enhance the image is crucial in order to improve the image quality. Segmentation techniques for Magnetic Resonance Imaging (MRI) of the brain are one of the methods used by radiographer to detect any abnormality that has happened specifically for the brain. The method is used to identify important regions in brain such as white matter (WM), grey matter (GM) and cerebrospinal fluid spaces (CSF). The clustering method known as Enhanced Adaptive Fuzzy K-means (EAFKM) is proposed to be used in this project as a tool to classify the three regions. The results are then compared with fuzzy C-means clustering (FCM) and adaptive fuzzy k-means (AFKM).The segmented image is analyzed both qualitative and quantitative. The proposed method provides better visual quality of the image and minimum Mean Square Error.
机译：医学图像被广泛用于计划患者的进一步治疗。但是，图像有时会被噪声破坏，这种噪声通常在存储或传输图像时存在或发生。因此，增强图像的需求对于改善图像质量至关重要。大脑磁共振成像（MRI）的分割技术是放射线照相技术用来检测专门针对大脑的任何异常的方法之一。该方法用于识别大脑中的重要区域，例如白质（WM），灰质（GM）和脑脊髓液空间（CSF）。提出了一种称为增强型自适应模糊K均值（EAFKM）的聚类方法，将其作为对这三个区域进行分类的工具。然后将结果与模糊C均值聚类（FCM）和自适应模糊k均值（AFKM）进行比较。对分割后的图像进行定性和定量分析。所提出的方法提供了更好的图像视觉质量和最小的均方误差。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号