Abstract: The invention discloses a multi-threshold segmentation method for medical images based on an improved salp swarm algorithm. A two-dimensional histogram is established by means of a grayscale image of a medical image and a non-local mean image, then a salp swarm algorithm is used to determine thresholds selected by a Kapur entropy-based threshold method, and the salp swarm algorithm is improved and mutated by an individual-linked mutation strategy during the threshold selection process to avoid local optimization, so that the segmentation effect on the medical image is optimized; and the method has the advantages of good robustness and high accuracy.

Abstract: The present disclosure relates to a kit for detecting dust mite component-specific antibodies, and belongs to the technical field of antibody detection kits. The kit comprises a biotin-polystreptavidin-biotin-dust mite antigen-coated nitrocellulose (NC) membrane, a washing solution, an alkaline phosphatase (ALP)-labeled secondary antibody solution for dust mite component-specific antibodies, and a substrate solution. The kit may be used in cooperation with a fully automated instrument.

Abstract: The disclosure is a medical disease feature selection method based on an improved salp swarm algorithm. The improved salp swarm algorithm is used to optimize a feature selection problem, the accuracy of the mentioned method is estimated by means of a transfer function and classification by a K-nearest neighbor algorithm, and the salp swarm algorithm is improved by using a self-adapted control parameter and an elite grey wolf ruling policy, so that it helps the algorithm avoid premature convergence in the optimization process and jump out of local optimum, thereby achieving a target of the algorithm with the smallest selected feature quantity and the highest classification precision. The method has the advantages of high rate of convergence, higher classification precision and better robustness.

Abstract: The invention discloses a multi-threshold segmentation method for medical images based on an improved salp swarm algorithm. A two-dimensional histogram is established by means of a grayscale image of a medical image and a non-local mean image, then a salp swarm algorithm is used to determine thresholds selected by a Kapur entropy-based threshold method, and the salp swarm algorithm is improved and mutated by an individual-linked mutation strategy during the threshold selection process to avoid local optimization, so that the segmentation effect on the medical image is optimized; and the method has the advantages of good robustness and high accuracy.