Department of Computer Science and Applications, Gandhigram Rural Institute, Gandhigram, Tamilnadu 624302, India.
Comput Biol Med. 2011 Aug;41(8):716-25. doi: 10.1016/j.compbiomed.2011.06.008. Epub 2011 Jul 2.
In this work we propose two brain extraction methods (BEM) that solely depend on the brain anatomy and its intensity characteristics. Our methods are simple, unsupervised and knowledge based. Using an adaptive intensity thresholding method on the magnetic resonance images of head scans, a binary image is obtained. The binary image is labeled using the anatomical facts that the scalp is the boundary between head and background, and the skull is the boundary separating brain and scalp. A run length scheme is applied on the labeled image to get a rough brain mask. Morphological operations are then performed to obtain the fine brain on the assumption that brain is the largest connected component (LCC). But the LCC concept failed to work on some slices where brain is composed of more than one connected component. To solve this problem a 3-D approach is introduced in the BEM. Experimental results on 61 sets of T1 scans taken from MRI scan center and neuroimage web services showed that our methods give better results than the popular methods, FSL's Brain Extraction Tool (BET), BrainSuite's Brain Surface Extractor (BSE) gives results comparable to that of Model-based Level Sets (MLS) and works well even where MLS failed. The average Dice similarity index computed using the "Gold standard" and the specificity values are 0.938 and 0.992, respectively, which are higher than that for BET, BSE and MLS. The average processing time by one of our methods is ≈1s/slice, which is smaller than for MLS, which is ≈4s/slice. One of our methods produces the lowest false positive rate of 0.075, which is smaller than that for BSE, BET and MLS. It is independent of imaging orientation and works well for slices with abnormal features like tumor and lesion in which the existing methods fail in certain cases.
在这项工作中，我们提出了两种仅依赖于大脑解剖结构及其强度特征的脑提取方法（BEM）。我们的方法简单、无监督且基于知识。使用磁共振成像头部扫描的自适应强度阈值方法，获得二进制图像。使用头皮是头部和背景之间的边界以及颅骨是大脑和头皮之间的边界的解剖事实对二进制图像进行标记。应用游程长度方案对标记的图像进行处理，以获得粗略的大脑掩模。然后进行形态操作，假设大脑是最大连通分量（LCC），从而获得精细的大脑。但是，LCC 概念在一些切片上无法工作，其中大脑由多个连通分量组成。为了解决这个问题，在 BEM 中引入了 3-D 方法。从 MRI 扫描中心和神经影像网络服务获得的 61 组 T1 扫描的实验结果表明，我们的方法比流行的方法（FSL 的脑提取工具（BET）、BrainSuite 的脑表面提取器（BSE））的结果更好，与基于模型的水平集（MLS）的结果相当，并且即使在 MLS 失败的情况下也能很好地工作。使用“黄金标准”计算的平均 Dice 相似性指数和特异性值分别为 0.938 和 0.992，高于 BET、BSE 和 MLS。我们的一种方法的平均处理时间约为 1s/slice，小于 MLS 的 4s/slice。我们的一种方法产生的假阳性率最低，为 0.075，小于 BSE、BET 和 MLS 的假阳性率。它独立于成像方向，对于存在异常特征（如肿瘤和病变）的切片效果很好，而现有方法在某些情况下会失败。
