To isolate the brain from non-brain tissues using a fully automatic method may be affected by the presence of radio frequency non-homogeneity of MR images (MRI), regional anatomy, MR sequences, and the subjects of the study. In order to automate the brain tumor (Glioblastoma) detection, we proposed a novel approach of skull stripping for axial slices derived from MRI. Then, the brain tumor was detected using multi-level threshold segmentation based on histogram analysis. Skull-stripping method, was applied by adaptive morphological operations approach. This is considered an empirical threshold by calculation of the area of brain tissue, iteratively. It was employed on the registration of non-contrast T1-weighted (T1-WI) and its corresponding fluid attenuated inversion recovery sequence. Then, we used multi-thresholding segmentation (MTS) method which is proposed by Otsu. We calculated the performance metrics based on the similarity coefficients for patients (n = 120) with tumor. The adaptive algorithm of skull stripping and MTS of segmented tumors were achieved efficient in preliminary results with 92 and 80 % of Dice similarity coefficient and 0.3 and 25.8 % of false negative rate, respectively. The adaptive skull stripping algorithm provides robust skull-stripping results, and the tumor area for medical diagnosis was determined by MTS.

中文翻译：

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定量评估坚固的颅骨剥离和肿瘤检测应用于轴向MR图像。

使用全自动方法将大脑与非脑组织隔离可能会受到MR图像（MRI），区域解剖结构，MR序列和研究对象的射频不均匀性的影响。为了自动检测脑肿瘤（胶质母细胞瘤），我们提出了一种新的颅骨剥离方法，用于从MRI提取轴向切片。然后，使用基于直方图分析的多级阈值分割检测脑肿瘤。通过自适应形态学运算方法应用了颅骨剥离法。通过反复计算脑组织的面积，这被认为是经验阈值。它用于非对比度T1加权（T1-WI）的配准及其相应的流体衰减反演恢复序列。然后，我们使用了Otsu提出的多阈值分割（MTS）方法。我们基于患有肿瘤的患者（n = 120）的相似性系数计算了性能指标。初步结果有效地实现了分割肿瘤的颅骨剥离和MTS自适应算法，Dice相似系数分别为92％和80％，假阴性率分别为0.3％和25.8％。自适应颅骨剥离算法提供了可靠的颅骨剥离结果，并且通过MTS确定了用于医学诊断的肿瘤区域。初步结果有效地实现了分割肿瘤的颅骨剥离和MTS自适应算法，Dice相似系数分别为92％和80％，假阴性率分别为0.3％和25.8％。自适应颅骨剥离算法提供了可靠的颅骨剥离结果，并且通过MTS确定了用于医学诊断的肿瘤区域。初步结果有效地实现了分割肿瘤的颅骨剥离和MTS自适应算法，Dice相似系数分别为92％和80％，假阴性率分别为0.3％和25.8％。自适应颅骨剥离算法提供了可靠的颅骨剥离结果，并且通过MTS确定了用于医学诊断的肿瘤区域。