For accurate segmentation of abnormal nuclei in liquid-based cervical cell images

a new nuclei segmentation method is proposed

which uses adaptive and local strategies. The adaptive stage detects each nucleus region approximately by applying an efficient adaptive thresholding algorithm that uses intensity and texture information. The local stage refines each coarse segment within its local neighborhood by using a Poisson distribution based graph cuts

which utilizes boundary and region information. The proposed method is applied to Hematoxylin & Eosin stained liquid-based cervical cell images. The results show that the proposed method achieves a speed of 1.6 s per image

and significantly outperforms a state-of-the-art method by Li et al in 2012 in terms of nuclei detection rate and abnormal nuclei segmentation accuracy

both with a 19.7% improvement.