Thyroid Cancer Cells Boundary Location by a Fuzzy Edge Detection Method

2. Obtain the gray-level gradient magnitude. 3. Derive the threshold surface by deforming the original image gray-level surface. 4. The threshold surface interpolation. 5. Segmentation based on the threshold surface. However, incomplete cells are still not detected reliably with this method. Double counting is sometimes occurred. In this paper, a Fuzzy Edge Detection Method is proposed. It is based on the Generalized Fuzzy Operator (GFO) [6]. It enhances those cells whose gray-level is of the cells. 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. based on an improved Generalized Fuzzy Operator. The In this paper, a Fuzzy Edge Detection Method is used and is to the background and produces boundary method enhances the nuclei and effectively separates the cells from the background.