Efficient segmentation framework of cell images in noise environments

We propose an efficient segmentation method that exploits local information for automated cell segmentation. This method introduces a new criterion function based on statistical structure of the objects in cell image. Each pixel is initially assigned to the most probable region and then the pixel assignment process is iteratively updated by a new criterion function until steady state is reached. We apply the proposed method to cervical cell images as well as the corresponding noisy images that are contaminated by Gaussian noise. The performance of the proposed method is evaluated based on the results from both normal and noisy cell images.