一種魯棒的人腦組織核磁共振圖像分割算法研究
A Robust Method for Segmentation of Human Brain Tissue from Magnetic Resonance Images
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摘要: 自動的人腦核磁共振(MR)圖像分割是許多醫(yī)學圖像應用的關(guān)鍵問題.該文提出了一種有效的自動腦核磁共振圖像的分割方法框架體系,腦MR分割框架體系由3個處理步驟構(gòu)成.首先,采用基于水平集的方法將MR圖像中非腦組織剔除,從腦圖像中提取大腦組織結(jié)構(gòu).然后,對MR腦結(jié)構(gòu)圖像進行灰度不均勻性校正.最后,該算法采用最大后驗分類器可以將人腦組織分為腦白質(zhì)、腦灰質(zhì)、腦髓液.在實驗中對大量的MR腦圖像數(shù)據(jù)應用該分割算法.實驗結(jié)果充分證明該方法的有效性.這種分割算法適用于人腦核磁圖像分析的各種實際臨床應用.
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關(guān)鍵詞:
- 核磁共振成像; 偏場校正; 水平集; 馬爾可夫隨機場; 分割
Abstract: Automatic segmentation of brain magnetic resonance images is a critical problem in many medical imaging applications. In this paper, a robust automated segmentation algorithm is presented for the brain magnetic resonance images. The segmentation framework is composed of three stages. First, it uses level set method to perform the brain stripping operation. In the second stage, it compensates for nonuniformity in the brain image based on computing estimates of tissue intensity variation. Finally, a maximum aposteriori classifier is used to partition the brain into gray matter, white matter, and cerebrospinal fluid. The proposed method has been tested using magnetic resonance dada. This algorithm may be applied to various research and clinical investigations in which brain segmentation and volume measurement involving Magnetic resonance images dada are needed. -
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