Automated Sperm Head Detection Using Intersecting Cortical Model Optimised by Particle Swarm Optimization.

ABSTRACT: In human sperm motility analysis, sperm segmentation plays an important role to determine the location of multiple sperms. To ensure an improved segmentation result, the Laplacian of Gaussian filter is implemented as a kernel in a pre-processing step before applying the image segmentation process to automatically segment and detect human spermatozoa. This study proposes an intersecting cortical model (ICM), which was derived from several visual cortex models, to segment the sperm head region. However, the proposed method suffered from parameter selection; thus, the ICM network is optimised using particle swarm optimization where feature mutual information is introduced as the new fitness function. The final results showed that the proposed method is more accurate and robust than four state

SUBMITTER: Tan WC 

PROVIDER: S-EPMC5025108 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature