Microscopic Image Segmentation with Two-Level Enhancement of Feature Discriminability
Microscopic cellular image segmentation has become one of the most important routine procedures in modern biological applications. The segmentation task is non-trivial, however, mainly due to imaging artifacts causing highly inhomogeneous appearances of cell nuclei and background with large intensity variations within and across images. Such inconsistent appearance profiles would cause feature overlapping between cell nuclei and background pixels and hence lead to misclassifiation. In this paper, we present a novel method for automatic cell nucleus segmentation, focusing on tackling the intensity inhomogeneity issue. A two-level approach is designed to enhance the discriminative power of intensity features, by first a reference-based intensity normalization for reducing the inter-image variations, and then a further localized object discrimination for overcoming the intra-image variations. The proposed method is evaluated on three different sets of 2D fluorescence microscopy images, and encouraging performance improvements over the state-of-the-art results are obtained.