Active Contour with Adaptive Timestep for Brain Tumor Segmentation
Bijay Kumar, Rutuparna Panda, Sanjay Agrawal · 2022 IEEE 7th International conference for Convergence in Technology (I2CT) · 2022
Active contours with level set have been widely used across various image segmentation applications. Leakage of the active contour around weak edges is a major challenge, specially for medical image segmentation. One of the factors responsible for the leakage is a fixed time-step used in the discrete implementation of the active contour evolution. A smaller time-step results in a slow-steady evolution of the contour increasing the overall evolution time till its convergence. Whereas a larger time-step may result in skipping over weak edges resulting in erroneous segmentation results. Therefore an adaptive time-step scheme is proposed in this work. This scheme manages a larger time-step when contour is evolving in the homogeneous regions, while it updates to lower time-steps as the contour gets on reaching the boundary. It continuously adapts the time-step using a locally explored feature of the contour. This strategy is implemented on an edge-based active contour in this work. It is then evaluated on a MRI dataset having three kinds of tumors. The results show improvement in segmentation performance over the fixed time-step approach of the original edge-based active contour. Our results are also compared with a recent CNN-based segmentation on the same dataset. It is found that our method gives better tumor delineation results measured in terms of Dice Similarity Coefficient.