模糊c均值与相关模板和活动轮廓的融合在弥散加权MRI脑损伤分割中的应用

A. F. Muda, N. Saad, N. Waeleh, A. Abdullah, Low Yin Fen
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引用次数: 8

摘要

本研究提出了一种基于模糊c均值(FCM)的弥散加权磁共振图像(DWI)脑损伤自动检测与分割方法。由于噪声和强度的不均匀性,FCM技术不能产生准确的结果。利用活动轮廓线和相关模板相结合的方法克服了这一问题。脑病变为急性中风和实体瘤型高信号病变,坏死和慢性中风型低信号病变。通过Jaccard (AO)、Dice(骰子)、假阴性率(FNR)和假阳性率(FPR)对所提出的分析框架进行了验证。与基于活动轮廓的FCM相比,基于关联模板的FCM具有更高的精度。Jaccard、FPR、FNR和Dice指数分别为0.547、0.258、0.192和0.687。该方法还可以精确地分割病灶。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Integration of Fuzzy C-Means with Correlation Template and Active Contour for Brain Lesion Segmentation in Diffusion-Weighted MRI
This study proposed automatic detection and segmentation of brain lesion in diffusion-weighted magnetic resonance images (DWI) based on Fuzzy C-Means (FCM). Due to noises and intensity inhomogeneity, FCM technique fails in producing accurate results. Active contour and correlation template are integrated to overcome this problem. The brain lesions are acute stroke and solid tumor foe hyperintense lesions, and necrosis and chronic stroke for hypointense lesions. The proposed analysis framework has been validated by using Jaccard (AO), Dice, false negative rate (FNR) and false positive rate (FPR). FCM with correlation template provides more accurate results compared with FCM with active contour. The results are 0.547, 0.258, 0.192 and 0.687 for Jaccard, FPR, FNR and Dice indices. This method also can segment the lesions precisely.
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