Abstract
Accurate identification of cell nuclei and their tracking using three dimensional (3D) microscopic images is a demanding task in many biological studies. Manual identification of nuclei centroids from images is an error-prone task, sometimes impossible to accomplish due to low contrast and the presence of noise. Nonetheless, only a few methods are available for 3D bioimaging applications, which sharply contrast with 2D analysis, where many methods already exist. In addition, most methods essentially adopt segmentation for which a reliable solution is still unknown, especially for 3D bio-images having juxtaposed cells. In this work, we propose a new method that can directly extract nuclei centroids from fluorescence microscopy images. This method involves three steps: (i) Pre-processing, (ii) Local enhancement, and (iii) Centroid extraction. The first step includes two variations: first variation (Variant-1) uses the whole 3D pre-processed image, whereas the second one (Variant-2) modifies the preprocessed image to the candidate regions or the candidate hybrid image for further processing. At the second step, a multiscale cube filtering is employed in order to locally enhance the pre-processed image. Centroid extraction in the third step consists of three stages. In Stage-1, we compute a local characteristic ratio at every voxel and extract local maxima regions as candidate centroids using a ratio threshold. Stage-2 processing removes spurious centroids from Stage-1 results by analyzing shapes of intensity profiles from the enhanced image. An iterative procedure based on the nearest neighborhood principle is then proposed to combine if there are fragmented nuclei. Both qualitative and quantitative analyses on a set of 100 images of 3D mouse embryo are performed. Investigations reveal a promising achievement of the technique presented in terms of average sensitivity and precision (i.e., 88.04% and 91.30% for Variant-1; 86.19% and 95.00% for Variant-2), when compared with an existing method (86.06% and 90.11%), originally developed for analyzing C. elegans images.
Highlights
The reliable extraction of nuclei centroids from cells using threedimensional (3D) digital images is an important task in various biological studies
We propose a novel method for the automated extraction of nuclei centroids from fluorescence microscopy images
The central region of a cell has higher luminance, which decreases gradually towards the cell/nuclei boundaries. To deal with these characteristics including the variable sizes of mouse embryonic cell nuclei, we propose a multiscale filtering that perform local optimization of multiple responses at every voxel
Summary
The reliable extraction of nuclei centroids from cells using threedimensional (3D) digital images is an important task in various biological studies. Parfitt et al used the same technique to regulate lineage allocation in the early mouse embryo [4] These methods improve cell analysis, manual cell marking by clicking computermouse is time-consuming and error-prone. Keller et al analyzed the embryogenesis of zebra fish by using specially designed digital scanned laser light sheet fluorescence microscopy (DSLM) [8] Their method applies recursive segmentation based on shapes and internal structures of cells. Oleh et al proposed a level–set–based technique for the segmentation and tracking cell nuclei of 2D human HeLa cells from fluorescence microscopy images [6] This method claimed to have an improved tracking performance, it was not tested with mouse embryo images
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