Active contour model-based segmentation algorithm for medical robots recognition

In this paper, an identifying and classifying algorithm is proposed to solve the problem of recognizing objects accurately and effectively. First, via image preprocessing, initial images are obtained via denoising, smoothness, and image erosion. Then, we use granularity analysis and morphology methods to recognize the objects. For small objects identification and to analyze the objects, we calculate four characteristics of each cell: area, roundness, rectangle factor, and elongation. Finally, we segment the cells using the modified active contour method. In addition, we apply chromatic features to recognize the blood cancer cells. The algorithm is tested on multiple collected clinical cases of blood cell images. The results prove that the algorithm is valid and efficient when recognizing blood cancer cells and has relatively high accuracy rates for identification and classification. The experimental results also certificate the effectiveness of the proposed method for extracting precise, continuous edges with limited human intervention, especially for images with neighboring or overlapping blood cells. In addition, the results of the experiments show that this algorithm can accelerate the detection velocity.