Changes of blood vessel morphology are closely related to disease. Diameter is the main parameter of blood vessel morphology and measurement of blood vessel diameter is beneficial to the screening and prevention of diseases. A method of measuring blood vessel diameter based on clustering algorithm was proposed to measure microvessels. Noise is present in most microvascular images (such as optical or photoacoustic microimaging) and the microimages can be enhanced by nonlinear transformation functions. Trained U-Net network model was used to achieve extraction of retinal vessels. The blood vessel diameter was measured by combining clustering algorithm and ray algorithm. Experiments show that this proposed algorithm was consistent with the traditional measurement results (P＞0.05). Compared with the traditional algorithm, the measurement accuracy of this algorithm was improved, and the measurement error was reduced from 4.21% to 2.27%, which meets the accuracy requirement of vascular measurement.