Mascarenhas Layse Ribeiro, Ribeiro Júnior Audenor Dos Santos, Ramos Rodrigo Pereira
Universidade Federal do Vale do São Francisco , Petrolina , PE , Brazil .
Einstein (Sao Paulo). 2020 Mar 9;18:eAO4948. doi: 10.31744/einstein_journal/2020AO4948. eCollection 2020.
To develop a computational algorithm applied to magnetic resonance imaging for automatic segmentation of brain tumors.
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.
The correlated correspondence between the segmentation obtained and the gold standard was 89.23%.
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.
开发一种应用于磁共振成像的计算算法，用于脑肿瘤的自动分割。
共使用了130张脑癌患者的磁共振图像，这些图像来自T1c、T2和FSPRG T1C序列，以及轴向、矢状和冠状平面。该算法采用对比度校正、直方图归一化和二值化技术，将相邻结构与大脑分离，并增强感兴趣区域。通过坐标检测和面积算术平均值进行自动分割。使用形态学算子消除不需要的元素，并重建肿瘤的形状和纹理。将结果与两名放射科医生的手动分割结果进行比较，以确定所实施算法的有效性。
获得的分割结果与金标准之间的相关对应率为89.23%。
基于两种创新方法，即检测磁共振图像上的脑极端部位并排除非肿瘤组织，无需用户交互即可自动定位和定义肿瘤区域。
