MR brain image segmentation using fuzzy clustering
MR brain image segmentation using fuzzy clustering
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DOI：
10.1109/fuzzy.1999.793060
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发表时间：
1999
期刊：
影响因子：
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通讯作者：
O. Yoon;Dong-Min Kwak;Dong-Whee Kim;Kil-Houm Park
中科院分区：
文献类型：
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作者：
O. Yoon;Dong-Min Kwak;Dong-Whee Kim;Kil-Houm Park
在解剖学方面，磁共振成像（MR）为医学检查提供了比其他医学图像（如x射线、超声波和CT图像）更准确的信息。本文提出了一种轴向磁共振脑图像的自动分割和病灶检测算法。为了减少组织分类的计算时间，本文提出的分割算法分为两步。首先，通过阈值分割、形态学操作和标记算法提取大脑区域；第二步，使用模糊c均值（FCM）算法检测大脑中的白质、灰质和脑脊液。新的病灶检测算法利用了解剖学知识和局部对称性。定义了一个对称度量来量化MRI切片的正态性，这是基于像素的数量，矩不变量和傅里叶描述子。该方法已应用于40个正常和异常切片。实验结果表明，所提出的分割算法适用于对大量轴向脑MR数据进行分类，也表明所提出的病灶检测算法是成功的。
In anatomical aspects, magnetic resonance (MR) imaging offers more accurate information for medical examination than other medical images such as X-ray, ultrasonic and CT images. In this paper, an automated segmentation and lesion detection algorithm are proposed for axial MR brain images. The proposed segmentation algorithm consists of two steps in order to reduce computation time for classifying tissues. In the first step, the cerebrum region is extracted by using thresholding, morphological operation, and labeling algorithm. In the second step, white matter, gray matter, and cerebrospinal fluid in the cerebrum are detected using fuzzy c-means (FCM) algorithm. The new lesion detection algorithm uses anatomical knowledge and local symmetry. A symmetric measure is defined to quantify the normality of MRI slice, which is based on the number of pixels, moment invariants, and Fourier descriptors. The proposed method has been applied to forty normal and abnormal slices. The experimental results show that the proposed segmentation algorithm is appropriate for classifying a large amount of axial brain MR data, and also show that the proposed lesion detection algorithm is successful.