Automatic segmentation of brain tumors in magnetic resonance imaging

ABSTRACT Objective To develop a computational algorithm applied to magnetic resonance imaging for automatic segmentation of brain tumors. Methods A total of 130 magnetic resonance images were used in the T1c, T2 and FSPRG T1C sequences and in the axial, sagittal and coronal planes of patients with brain cancer. The algorithms employed contrast correction, histogram normalization and binarization techniques to disconnect adjacent structures from the brain and enhance the region of interest. Automatic segmentation was performed through detection by coordinates and arithmetic mean of the area. Morphological operators were used to eliminate undesirable elements and reconstruct the shape and texture of the tumor. The results were compared with manual segmentations by two radiologists to determine the efficacy of the algorithms implemented. Results The correlated correspondence between the segmentation obtained and the gold standard was 89.23%. Conclusion It is possible to locate and define the tumor region automatically with no the need for user interaction, based on two innovative methods to detect brain extreme sites and exclude non-tumor tissues on magnetic resonance images.

摘要
研究目的：开发一种应用于磁共振成像（magnetic resonance imaging）的脑肿瘤自动分割计算算法。
方法：本研究共纳入130例脑癌患者的磁共振成像数据，涵盖T1c、T2及FSPRG T1C序列，且包含轴位、矢状位及冠状位三种成像平面。所采用的算法通过对比度校正、直方图归一化及二值化技术，分离脑部邻近结构并强化感兴趣区域。自动分割通过坐标检测及区域算术均值计算完成，随后利用形态学算子剔除无关元素，并重构肿瘤的形态与纹理特征。将算法自动分割结果与两名放射科医师的手动分割结果进行对比，以评估所提算法的临床效能。
结果：本研究得到的分割结果与金标准的相关匹配度达89.23%。
结论：基于两种可检测脑部极端位点并剔除磁共振成像中非肿瘤组织的创新方法，本算法可在无需用户交互的前提下，自动完成肿瘤区域的定位与界定。