Cell Image Segmentation of Biomedical Microscopes
As an important research topic in the field of image recognition, microscopic cell image processing covers such subjects as image processing, pattern recognition, computer vision and so on. It has wide application value. Quantitative research on molecular level, exploring the pathological mechanism of diseases and revealing the origin and formation of life are the main development ideas in the field of biomedicine at present and in the future. The development from qualitative analysis to quantitative analysis of ultrastructure has gradually become the direction of biomedical research, which also means that there is a need for quantitative processing of electron microscopic images. Therefore, the study of image processing at the level of cell resolution is of great significance in the field of biomedicine, making it a fully automatic, non-human intervention, efficient and accurate computer-aided technology. In the cell image edge detection of Differential Interference Difference Microscope, the traditional cell edge detection algorithm based on gray information can not overcome the problems of uneven illumination, low contrast and unsatisfactory edge detection effect. A cell edge detection algorithm based on phase congruence is proposed in this paper. This algorithm improves the form of phase deviation weighting function, and improves the sensitivity of phase congruence algorithm in capturing local features. Experiments on edge detection based on neural stem cell image and red blood cell image show that cell edge detection algorithm based on phase congruence can provide more accurate edge information for cell image contour segmentation.