Segmentation of MR images using multispectral fusion approach : A study and an evaluation

Lamiche Chaabane, Moussaoui Abdelouahab
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Abstract

The paper presents a study and an evaluation of a novel unsupervised segmentation technique based aggregation approach and some possibility theory concepts. Information provided by different sources of MR images is extracted and modeled separately in each one using MPFCM (Modified Possibilistic Fuzzy C-Means) algorithm, extracted data obtained are combined with an operator which can managing the uncertainty and ambiguity in the images and the final segmented image is constructed in decision step. The efficiency of the proposed method is demonstrated by segmentation experiments using simulated MR Images.
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用多光谱融合方法分割磁共振图像:研究与评价
本文研究并评价了一种基于聚合方法和一些可能性理论概念的新型无监督分割技术。利用MPFCM (Modified possibilities Fuzzy C-Means,修正可能性模糊C-Means)算法对不同来源的磁共振图像信息进行提取和建模,将提取的数据与对图像中的不确定性和模糊性进行管理的算子相结合,在决策步骤中构造最终的分割图像。通过模拟MR图像的分割实验,验证了该方法的有效性。
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