Automatic detection of trichomonads based on an improved Kalman background reconstruction algorithm.

Automatic detection of trichomonads in leukorrhea provides important information for evaluating gynecological diseases. Traditional manual microscopy, which depends on the operator's expertise and subjective factors, has high false-positive rates (i.e., low specificity) and low efficiency. To date, there are many detection methods for biological cells based on morphological characteristics. However, the morphology of trichomonads changes, and its size is not fixed; moreover, they are similar to human leukocytes. Therefore, it is difficult to classify trichomonads based on morphological characteristics. In this study, a moving object detection method based on an improved Kalman background reconstruction algorithm is proposed to detect trichomonads automatically, considering the dynamic characteristics of trichomonads at room temperature. The experimental results show that the trichomonads can be accurately identified, and the phenomena of tailing and ghosts are eliminated. Furthermore, this algorithm easily adapts to continuous or sudden changes in light, focal length variation, and the impact of lens shift, and it has good robustness and only a moderate amount of calculation burden.