Feature Extraction of Human Viruses Microscopic Images Using Gray Level Co-occurrence Matrix

With the development of information technology in biomedical signal detection, processing and digital image signal processing, the role of automatic visual recognition becomes more important. In this paper, in order to effectively extract the feature information of human viruses (HV) microscopic images, an algorithm of HV microscopic image feature extraction and recognition using gray level co-occurrence matrix (GLCM) is proposed. Firstly, 20 pieces of microscopic images of human virus are obtained by using GLCM, and then the four texture feature parameters, entropy, energy, inertia moment and correlation are extracted utilizing the GLCM, and then HV image recognition is carried out. The experimental results show that the GLCM and extraction of image texture features can effectively identify the HV image, which can bring significance to the modern recognition and identification of HV.