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.