Thyroid cancer cells boundary location by a fuzzy edge detection method

Morphometric assessment of tumor cells is important in the prediction of biological behavior of thyroid cancer. In order to automate the process, the computer-based system has to recognize the boundary of the cells. Many methods for the boundary detection have appeared in the literature and some of them applied to microscopic slice analysis. However, there is no reliable method since the gray-levels in the nuclei are uneven and are similar to the background. In the paper, a fuzzy edge detection method is used and is based on an improved generalized fuzzy operator. The method enhances the nuclei and effectively separates the cells from the background.

[1]  R. F. Wagner,et al.  Fundamental correlation lengths of coherent speckle in medical ultrasonic images , 1988, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[2]  Chen Jianjun,et al.  A new algorithm of edge detection for color image: Generalized fuzzy operator , 1995 .

[3]  E.E. Pissaloux,et al.  Image Processing , 1994, Proceedings. Second Euromicro Workshop on Parallel and Distributed Processing.

[4]  Hui Zhu,et al.  Adaptive thresholding by variational method , 1998, IEEE Trans. Image Process..

[5]  R. Nevatia.  A Color Edge Detector and Its Use in Scene Segmentation , 1977 .

[6]  Jun Shen,et al.  An optimal linear operator for step edge detection , 1992, CVGIP Graph. Model. Image Process..

[7]  Alfred M. Bruckstein,et al.  A new method for image segmentation , 1988, [1988 Proceedings] 9th International Conference on Pattern Recognition.

[8]  Alan C. Bovik,et al.  On detecting edges in speckle imagery , 1988, IEEE Trans. Acoust. Speech Signal Process..

[9]  Geoff A. W. West,et al.  Segmentation of edges into lines and arcs , 1989, Image Vis. Comput..