基于偏差场估计的改进模糊聚类算法的磁共振治疗图像分割

L. Kumar
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引用次数: 0

摘要

治疗性磁共振图像分割是医学图像处理中的难点。在现实世界医学图像中出现了大量的问题。本文提出了一种基于模糊聚类技术的偏置场估计方法。用偏置场估计扫描损坏和盐纸噪声。简单易行的对给定医学图像数据库中一定数量的聚类进行分类的先验固定技术。本文主要研究脑磁共振图像的分割和偏置场估计,涉及到模糊聚类算法。在新的改进技术中,评价了模糊均值分割白质和加里物质的能力。它为有效分割MRI数据和节省时间提供了额外的前景。高斯权值用于研究扫描图像聚类中特征向量的传递。实现了模糊聚类和模糊聚类的经验评价,并进行了偏置场估计。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
THERAPEUTIC MR IMAGE SEGMENTATION BASED ON UPGRADED FUZZY CLUSTERING ALGORITHM WITH BIAS FIELD ESTIMATION
Therapeutic MR image segmentation is difficult in medical image processing. There are huge of issue are come about in the actual world medical images. In this research paper, gives method bias field estimation based fuzzy clustering technique. Scan corrupted and saltand-paper noise using Bias field estimation. Easy and simple to classify a given medical image database over a certain number of cluster fixed a-priori technique. In this research article, segmentation and Bias field estimation of brain MR images and involved the fuzzy clustering algorithm. In new improved technique evaluates the ability of Fuzzy cMean to segment White and Gary matter. It delivers extra prospective for efficiently segmenting MRI data and time consuming. The Gaussian weights is explore the delivery of the feature vectors in the scan image clusters. The empirical evaluation UFCA and fuzzy clustering, with Bias field estimation is achieved.
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