Brain tumor segmentation algorithm based on pathology topological merging

Automatically segmenting the lesion of clinical data can aid doctors in diagnosis. The key issues with clinical brain tumor segmentation are partial volume effect, bias field, and noise interference. This paper proposes a novel segmentation algorithm based on pathology topological merging to solve the above problems. Here, an improved superpixel technique is used to group the pathology topological blocks, and the vector distance is refined to avoiding the problem of grouping pixels with small similarity near the tumor contour into the same region. Furthermore, meaningful pathology topological blocks are formed, and the entire brain tumor is segmented based on the pathology topological relationship and weight between pathology topological blocks. The proposed method is validated on the BraTS 2015 dataset and 123 patient images with brain tumors from a local hospital, and the mean Dice, Jaccard, Precision, and Recall values are 0.91, 0.92, 0.90, and 0.91, respectively, indicating that the proposed method can efficiently and accurately distinguish brain tumors from other tissues (such as edema). The method can overcome some defects (e.g. partial volume effect, bias field, and noise interference) in medical brain images while having practical clinical application prospects.