The Study Of Image Segmentation Technology Based On Sections Of Histiocyte Cells

With the progress of technology, the processing of medical biological cells image has been widely attention. In the study of biological cell images, the most important is to identify and extract cells form morphology. Research on this issue has significant impact for the cell3D reconstruction achievement under microscopy or ultramicroscopic, still can provide the basis for disease diagnosis, promote the quantitative analysis of cell information, research the transmission of cell internal information and the mutation of cell.The image segmentation of tissue cell can help researchers extract the target area or prospects from a complex scene, which researchers are interested in. In this research, at first the TopHat and BlackHat method are to be used to enhance the image, and then detect the edges of the image due to the iterative threshold method, and then dilation and erosion algorithm to be used to highlight the edge of the cell image and according to the size of the connected area of the binary cell image to remove the part of pseudo-region, and then link the cell’s edges of the binary image, to get the ideal segmentation result. Finally mark connected region in the image to analysis the image, and further to obtain the corresponding data of each marker for the cells image under a microscope as to provide a basis for further analysis. After tissue cell image segmentation’ the count, parameter calculation, calculation and analysis of area and morphological, analysis of the optical density and so on could been successfully came true.The selection of segmentation algorithm influence subsequent analysis that about the tissue volume measurement and morphological. The experiment algorithm is well detect the cell from the background of the cell image, the image edge detection more accurate than other methods, the desired purpose to be achieved. However, the presence of impurities in the interior of the cell area, causing a mutation of the grey value of the digital image, generated a large number of pseudo-edge or pseudo-region area in the cells image and increased the difficulty of edge detection. Therefore, the experiment has improved the segmentation algorithm to eliminate false edges and pseudo-region in the cell image. In addition, the presence of cell adhesion in the cell image, at the edge of the cell adhesion in segmentation is detected more obscure. The marked-based watershed algorithm has been improved to achive further segmentation of adhension cells.