Over the past few decades, extraction and visualization of flow features like vortices has gained tremendous importance and is employed in numerous applications. Several vortex detectors are available in literature that can identify vortices in most empirical and computational datasets. However, despite these efforts, uncertainties in empirical measurements often results in undesired vectors that cause clutter in visualization. Clutter would obscure vortex features and make it hard to understand complex flow behavior. Additionally, floating-point errors in vortex detector computations lead to false positives in vortex extraction. This thesis aims to solve aforementioned problems by implementing - a pre-processing technique to filter undesired vectors from empirical data and a threshold estimation technique to reduce the effect of floating-point errors in vortex extraction. Proposed methodologies are tested on several flow datasets of various sizes and turbulence intensities. Results indicate enhanced visualization by reducing clutter; also, they confirm improved robustness in vortex extraction.