CAJ | 학술논문

目的：磁共振成像（ magnetic resonance imaging ， MRI）对脑组织有较好的成像效果，但噪声、偏移场和部分容积效应（ partial volume effect ， PVE）的存在，使得全自动分割MRI图像面临一定的困难。模糊C均值（fuzzy C－means， FCM）聚类算法在脑组织分割中得到较广泛研究。本文以存在噪声和偏移场影响的脑MRI图像分割为应用背景，研究了大量相关方法，探讨FCM算法分割脑部图像的改进思想。方法本文主要研究了9种FCM算法的理论基础，并通过脑组织分割实验对各种算法进行了分析。结果比较了不同算法的优劣，给出各类算法直观及定量评价结果。结论偏移场和噪声对脑磁共振图像组织分类质量有明显影响。其中几种方法可以减弱这些不利影响，但由于难以选择合适的参数，其分类效果并不理想。如何合理利用空间信息在未来仍有较大研究价值。
목적：자공진성상（ magnetic resonance imaging ， MRI）대뇌조직유교호적성상효과，단조성、편이장화부분용적효응（ partial volume effect ， PVE）적존재，사득전자동분할MRI도상면림일정적곤난。모호C균치（fuzzy C－means， FCM）취류산법재뇌조직분할중득도교엄범연구。본문이존재조성화편이장영향적뇌MRI도상분할위응용배경，연구료대량상관방법，탐토FCM산법분할뇌부도상적개진사상。방법본문주요연구료9충FCM산법적이론기출，병통과뇌조직분할실험대각충산법진행료분석。결과비교료불동산법적우렬，급출각류산법직관급정량평개결과。결론편이장화조성대뇌자공진도상조직분류질량유명현영향。기중궤충방법가이감약저사불리영향，단유우난이선택합괄적삼수，기분류효과병불이상。여하합리이용공간신식재미래잉유교대연구개치。
Objective Magnetic resonance imaging ( MRI) provides high resolution for brain tissue , yet the presence of noise, bias field, and partial volume effect (PVE), make automatic segmentation of MRI image a challenge task .Fuzzy C-means ( FCM ) clustering algorithm is widely studied these years .This paper investigates different variants of FCM methods for brain tissue segmentation and explores its improvement , especially in the presence of noise and bias field in MRI images .Methods Nine variants of FCM methods are analyzed theoretically first .Then brain tissue segmentation experiments are done to evaluate these algorithms ’ performance .Results We compare the merits of different algorithms and give the qualitative and quantitative results . Conclusions Bias field and noise degrade the classification quality apparently .Though certain methods have the abilities to decrease the influence of noise and bias field, the difficulty of choosing the optimum parameters hinders their performance.Reasonable utilization of spatial information has research value in the future.