Local maximum detection for fully automatic classification of EM algorithm

T. Lerddararadsamee, Y. Jiraraksopakun
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引用次数: 4

Abstract

In this paper, we proposed a method for fully-automatic EM segmentation on brain MR images without a priori knowledge. Instead of manually predetermination on number of tissue classes, the proposed method automatically find mean intensities of distinct tissues from the histogram. The brain MR images were chosen to test our proposed method, but our method can, in fact, be general for other MR segmentations using EM with which the Gaussian mixture distribution of an image histogram holds. The results from our method suggested that a fully automatic segmentation using EM can be achieved with no significant difference in segmentation accuracy compared to the conventional EM.
局部最大值检测的全自动EM分类算法
本文提出了一种无需先验知识的全自动脑磁共振图像分割方法。该方法不需要人工预先确定组织类别的数量,而是从直方图中自动找到不同组织的平均强度。选择脑磁共振图像来测试我们提出的方法,但实际上,我们的方法可以用于使用图像直方图的高斯混合分布的EM的其他MR分割。结果表明,与传统EM相比,使用EM可以实现全自动分割,而分割精度没有显著差异。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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