Moldovanu Simona, Moraru Luminița, Biswas Anjan
Department of Chemistry, Physics and Environment, Faculty of Sciences and Environment, Dunărea de Jos University of Galaţi, 47 Domnească St., 800008, Galaţi, Romania.
Dumitru Moţoc High School, 15 Milcov St., 800509, Galaţi, Romania.
J Digit Imaging. 2015 Dec;28(6):738-47. doi: 10.1007/s10278-015-9776-6.
This paper proposes a new method for simple, efficient, and robust removal of the non-brain tissues in MR images based on an irrational mask for filtration within a binary morphological operation framework. The proposed skull-stripping segmentation is based on two irrational 3 × 3 and 5 × 5 masks, having the sum of its weights equal to the transcendental number π value provided by the Gregory-Leibniz infinite series. It allows maintaining a lower rate of useful pixel loss. The proposed method has been tested in two ways. First, it has been validated as a binary method by comparing and contrasting with Otsu's, Sauvola's, Niblack's, and Bernsen's binary methods. Secondly, its accuracy has been verified against three state-of-the-art skull-stripping methods: the graph cuts method, the method based on Chan-Vese active contour model, and the simplex mesh and histogram analysis skull stripping. The performance of the proposed method has been assessed using the Dice scores, overlap and extra fractions, and sensitivity and specificity as statistical methods. The gold standard has been provided by two neurologist experts. The proposed method has been tested and validated on 26 image series which contain 216 images from two publicly available databases: the Whole Brain Atlas and the Internet Brain Segmentation Repository that include a highly variable sample population (with reference to age, sex, healthy/diseased). The approach performs accurately on both standardized databases. The main advantage of the proposed method is its robustness and speed.
本文提出了一种基于无理掩码在二值形态学运算框架内进行滤波的简单、高效且稳健的去除磁共振图像中非脑组织的新方法。所提出的脑剥离分割基于两个无理的3×3和5×5掩码，其权重之和等于格雷戈里 - 莱布尼茨无穷级数提供的超越数π值。它能够保持较低的有用像素损失率。所提出的方法已通过两种方式进行测试。首先，通过与大津法、Sauvola法、Niblack法和伯恩森二值法进行比较和对比，验证其作为二值方法的有效性。其次，针对三种先进的脑剥离方法验证其准确性：图割法、基于Chan-Vese活动轮廓模型的方法以及单纯形网格和直方图分析脑剥离法。使用骰子系数、重叠和额外分数以及敏感性和特异性作为统计方法来评估所提出方法的性能。金标准由两位神经科专家提供。所提出的方法已在包含来自两个公开可用数据库（全脑图谱和互联网脑分割存储库）的216张图像的26个图像系列上进行测试和验证，这些数据库包含高度可变的样本群体（涉及年龄、性别、健康/患病情况）。该方法在两个标准化数据库上均能准确执行。所提出方法的主要优点是其稳健性和速度。
