中国医疗设备2017，Vol.32Issue(11)：61-65,5.DOI:10.3969/j.issn.1674-1633.2017.11.015
一种全自动的脑部MR图像分割算法
A Novel Automatic Segmentation Approach for Brain MR Image
摘要
Abstract
Objective Automated brain magnetic resonance (MR) image segmentation is a complex problem especially if accompanied by intensity inhomogeneity and noise. This paper proposed a modified fuzzy c-mean (MFCM) method which is used for the automatic segmentation of brain MR imaging.Methods The algorithm begined with a preprocessing step where we implemented automatic bias removal and contrast enhancement. This was followed by automated retrieval of mean intensity positions of various tissues detected. The corrected image was then passed on to an MFCM. The segmented result was further passed through neighborhood-based membership ambiguity correction which smooths the ambiguous boundaries and also removes pixel level noise between continuous regions of intensities.Results Brain Web normal brain simulated database with noise ranging from 0–9% and the inhomogeneity was 0 and 40% respectively. Qualitative evaluation Results showed that the proposed method could provide clearer boundaries than that without pre- and post-processing. Quantitative evaluation Results indicated that the improved active contour algorithm generated a higher degree of sensitivity, specificity and similarity than traditional FCM and FSL library tool-based software.Conclusion The proposed algorithm is of fully automatic segmentation, with faster computation and faster convergence of the Objective function, which makes it be a feasible method for automatic brain MR segmentation.关键词
脑部MR图像/全自动分割/同质滤波/峰值检测/模糊C均值Key words
brain magnetic resonance image/automatic segmentation/homomorphic filtering technique/peaks retrieval/fuzzy c-mean分类
信息技术与安全科学引用本文复制引用
缪正飞,陈广浩,高伟..一种全自动的脑部MR图像分割算法[J].中国医疗设备,2017,32(11):61-65,5.基金项目
国家自然科学青年基金(81601477). （81601477）
