Adaptive segmentation of cervical smear image based on GVF Snake model

The key of a computer-assisted diagnosis system for screening of cervical cancer is the accurate segmentation of cells. In this paper, an adaptive segmentation algorithm based on GVF Snake model is proposed to separate the nucleus from cervical smear model. We set the parameters of the model, and then use the model to segment the cervical cells based on the initial contour of nuclei. The segmentation results are evaluated, if the results meet the criterion, the segmentation process finishes, otherwise we adjust the parameter which is a weight in energy function when calculating GVF field and perform the segmentation procedure again. Adjusting the parameter by evaluating the segmentation results is an adaptive and sensible method with regard to the automatic segmentation. The experiment results show the effectiveness of the proposed approach in images having inconsistent staining and poor contrast.

[1]  Wang Li.  Research of immune image clustering algorithm based on space histogram , 2008 .

[2]  Doo Heon Song,et al.  Nucleus Segmentation and Recognition of Uterine Cervical Pap-Smears , 2009, RSFDGrC.

[3]  Xutong Niu,et al.  A semi-automatic framework for highway extraction and vehicle detection based on a geometric deformable model , 2006 .

[4]  Junaed Sattar.  Snakes , Shapes and Gradient Vector Flow , 2022 .

[5]  Jia Yin-bob.  Cell image segmentation method based on partial adaptive thresholds , 2009 .

[6]  Jinshan Tang.  A multi-direction GVF snake for the segmentation of skin cancer images , 2009, Pattern Recognit..

[7]  Demetri Terzopoulos,et al.  Snakes: Active contour models , 2004, International Journal of Computer Vision.

[8]  Bill Degrado,et al.  Engineering and design. , 1995, Current opinion in structural biology.

[9]  Hai-Shan Wu,et al.  Region growing segmentation of textured cell images , 1996 .

[10]  Fan Zhang,et al.  Contour extraction of gait recognition based on improved GVF Snake model , 2012, Comput. Electr. Eng..

[11]  Jinyong Cheng,et al.  Segmentation of medical images with Canny operator and GVF snake model , 2008, 2008 7th World Congress on Intelligent Control and Automation.

[12]  Xavier Bresson,et al.  Geodesic Active Fields - A Geometric Framework for Image Registration , 2010 .

[13]  Nobuyuki Otsu,et al.  ATlreshold Selection Method fromGray-Level Histograms , 1979 .

[14]  Christophoros Nikou,et al.  Watershed-based segmentation of cell nuclei boundaries in Pap smear images , 2010, Proceedings of the 10th IEEE International Conference on Information Technology and Applications in Biomedicine.

[15]  James M. Keller,et al.  Automated Geospatial Conflation of Vector Road Maps to High Resolution Imagery , 2009, IEEE Transactions on Image Processing.

[16]  J.S. Jin,et al.  Automatic cell segmentation in microscopic color images using ellipse fitting and watershed , 2010, IEEE/ICME International Conference on Complex Medical Engineering.

[17]  J. Chassery,et al.  An iterative region-growing process for cell image segmentation based on local color similarity and global shape criteria. , 1986, Analytical and quantitative cytology and histology.