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基于 SOM-FCM 方法和 3D 统计描述符的脑 MRI 分割。

Segmentation of brain MRI using SOM-FCM-based method and 3D statistical descriptors.

机构信息

Communications Engineering Department, University of Malaga, 29004 Malaga, Spain.

出版信息

Comput Math Methods Med. 2013;2013:638563. doi: 10.1155/2013/638563. Epub 2013 May 14.

DOI:10.1155/2013/638563
PMID:23762192
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3666364/
Abstract

Current medical imaging systems provide excellent spatial resolution, high tissue contrast, and up to 65535 intensity levels. Thus, image processing techniques which aim to exploit the information contained in the images are necessary for using these images in computer-aided diagnosis (CAD) systems. Image segmentation may be defined as the process of parcelling the image to delimit different neuroanatomical tissues present on the brain. In this paper we propose a segmentation technique using 3D statistical features extracted from the volume image. In addition, the presented method is based on unsupervised vector quantization and fuzzy clustering techniques and does not use any a priori information. The resulting fuzzy segmentation method addresses the problem of partial volume effect (PVE) and has been assessed using real brain images from the Internet Brain Image Repository (IBSR).

摘要

当前的医学成像系统提供了极好的空间分辨率、高组织对比度和多达 65535 个强度级别。因此，为了在计算机辅助诊断 (CAD) 系统中使用这些图像，需要利用图像处理技术来挖掘图像中包含的信息。图像分割可以定义为对图像进行分割以限定大脑中存在的不同神经解剖组织的过程。在本文中，我们提出了一种使用从体图像中提取的 3D 统计特征的分割技术。此外，所提出的方法基于无监督矢量量化和模糊聚类技术，并且不使用任何先验信息。所得到的模糊分割方法解决了部分体积效应 (PVE) 的问题，并使用来自互联网大脑图像库 (IBSR) 的真实大脑图像进行了评估。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/06e4/3666364/fd83cd6cc0ba/CMMM2013-638563.alg.001.jpg
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