A modified fuzzy C-means classification method using a multiscale diffusion filtering scheme.
A modified fuzzy C-means classification method using a multiscale diffusion filtering scheme.
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DOI：
10.1016/j.media.2008.06.014
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发表时间：
2009-04
影响因子：
10.9
通讯作者：
Fei B
中科院分区：
文献类型：
--
作者：
Wang H;Fei B
提出了一种全自动、多尺度的模糊C均值（MsFCM）分类方法。我们使用扩散滤波器来处理MR图像，并构建多尺度图像序列。一个多尺度模糊C-均值分类方法是应用沿着从粗到细的水平的尺度。传统的模糊C-均值（FCM）方法的目标函数进行修改，允许多尺度分类处理，从一个粗尺度的结果监督下一个细尺度的分类。该方法是强大的噪声和低对比度的MR图像，因为它的多尺度扩散滤波方案。将该方法与传统的FCM方法和改进的FCM方法进行了比较。对具有各种对比度的合成图像和麦吉尔脑MR图像数据库进行验证研究。我们的MsFCM方法始终优于传统的FCM和MFCM方法。MsFCM方法实现了大于90%的重叠率，通过地面实况验证。在真实的MR图像上的实验结果验证了该方法的有效性。我们的多尺度模糊c-均值分类方法是准确的和强大的各种MR图像。它可以为神经成像和其他应用提供定量工具。
A fully automatic, multiscale fuzzy c-means (MsFCM) classification method for MR images is presented in this paper. We use a diffusion filter to process MR images and to construct a multiscale image series. A multiscale fuzzy C-means classification method is applied along the scales from the coarse to fine levels. The objective function of the conventional fuzzy c-means (FCM) method is modified to allow multiscale classification processing where the result from a coarse scale supervises the classification in the next fine scale. The method is robust for noise and low-contrast MR images because of its multiscale diffusion filtering scheme. The new method was compared with the conventional FCM method and a modified FCM (MFCM) method. Validation studies were performed on synthesized images with various contrasts and on the McGill brain MR image database. Our MsFCM method consistently performed better than the conventional FCM and MFCM methods. The MsFCM method achieved an overlap ratio of greater than 90% as validated by the ground truth. Experiments results on real MR images were given to demonstrate the effectiveness of the proposed method. Our multiscale fuzzy c-means classification method is accurate and robust for various MR images. It can provide a quantitative tool for neuroimaging and other applications.
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影响因子：
10.6
作者：
Liew, AWC;Yan, H
通讯作者：
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