Modified magnetic resonance image based parcellation method for cerebral cortex using successive fuzzy clustering and boundary detection
Modified magnetic resonance image based parcellation method for cerebral cortex using successive fuzzy clustering and boundary detection
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DOI：
10.1114/1.1557973
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发表时间：
2003-04-01
影响因子：
3.8
通讯作者：
Kim, SI
中科院分区：
文献类型：
--
作者：
Yoon, U;Lee, JM;Kim, SI
开发准确和可重复的人脑切片可以用来解决大脑中复杂的结构-功能关系。我们提出了一种改进的parcellation方法，使用连续模糊c均值（sFCM）和边界检测算法提供可靠和可重复的感兴趣区域。该方法同时显示用于识别脑沟标志的原始脑图像和用于参考脑沟模式的组织分类图像。利用半自动区域生长法提取整个大脑区域，然后利用sFCM将其分为灰质、白色物质和脑脊液。参考其他先前的研究，显示灰质与白色物质的体积比发现分类效率提高（常规FCM：0.80 +/- 0.12 vs. sFCM：1.57 +/- 0.18）。通过回归分析估计的评分者间可靠性表明，所提出的方法比传统方法更可靠和可重复[分析：相关系数（CC）= 0341，Sig. = 0.335 vs.拟定方法：CC = 0.816，Sig. = 0.004]。全脑体积与被包裹物体体积的比值可用于精神分裂症、强迫症等精神疾病的病理检测。(C)2003生物医学工程学会。
Development of the accurate and reproducible parcellation of the human brain can be used to resolve the complex structure-functional relationships in the brain. We propose a modified parcellation method that provides the reliable and reproducible regions of interest using successive fuzzy c-means (sFCM) and boundary-detection algorithm. This method displays simultaneously both original brain image for identifying the sulcal landmarks and its tissue-classified image for referring to patterns of sulci. The whole cerebral region is extracted by the semiautomated region growing method and then classified to gray matter, white matter, and cerebrospinal fluid by sFCM. Referred to the other previous researches, the volume ratio of gray matter to white matter was shown to find that the efficiency of classification was improved (conventional FCM: 0.80 +/- 0.12 vs. sFCM: 1.57 +/- 0.18). Inter-rater reliability, estimated by the regression analysis, demonstrated that the proposed method was more reliable and reproducible than conventional methods [ANALYZE: correlation coefficient (CC) = 0341, Sig. = 0.335 vs. proposed method: CC = 0.816, Sig. = 0.004]. The volume ratio of the whole cerebrum to the parceled object can be used to investigate structural abnormalities for the pathological detection of the various mental diseases such as schizophrenia, obsessive-compulsive disorder. (C) 2003 Biomedical Engineering Society.