基于神经网络的模糊c均值聚类算法的MRI分割

P. M. Birgani, M. Ashtiyani, S. Asadi
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引用次数: 46

摘要

磁共振成像(MRI)相对于其他诊断图像方式的优点是其高空间分辨率和对软组织的良好识别。许多神经系统疾病会改变脑组织的形状、体积和分布;MRI是检查这些条件的首选成像方式,这些条件需要分割成不同的强度类别,这些强度类别被认为是生物组织的最佳可用表示。有必要对MRI进行计算机分析,如肿瘤的精确描绘和可靠的,可重复的图像分割。本文的目的是提出一种基于神经网络的FCM聚类算法用于MRI分割。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
MRI Segmentation Using Fuzzy C-means Clustering Algorithm Basis Neural Network
The advantages of magnetic resonance imaging (MRI) over other diagnostic image modalities are its high spatial resolution and excellent discrimination of soft tissues. Many neurological conditions alter the shape, volume, and distribution of brain tissue; MRI is the preferred imaging modality for examining these conditions which requires segmentation into different intensity classes which are regarded as the best available representations for biological tissues. There is a need for computer analysis of MRI such as precise delineation of tumors and reliable, reproducible segmentation of images. The aim of this work is to propose a FCM clustering algorithm basis neural network for MRI segmentation.
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