Nuclei Segmentation of Cervical Cell Images Based on Intermediate Segment Qualifier

The accurate nuclei segmentation of cervical cell images is a very vital step in automated cervical diseases diagnosis. However, segmentation challenges exist because of problems such as nuclei embedment into cytoplasm folding or overlapping areas, impurity interference, low contrast and nuclei variation in shape and size. These problems can cause the nuclei segmentation results not so ideal. This paper presents an automated method for cells nuclei detection in cervical cell images. We propose an intermediate segment qualifier to categorize the nuclei segmentation results after the nuclei segmentation based on the integration of convolutional neural network and simple linear iterative clustering superpixel method. Then we apply a gradient vector flow snake model for further refinement. We evaluate the proposed method using the ISBI 2014 challenge dataset. In the experiments, we demonstrate that our method performs well and is preferable to the state-of-the-art approaches.