Microscopic images provide lots of useful information for modern diagnosis and biological research. However, due to the unstable lighting condition during image capturing, two main problems, i.e., high-level noises and low image contrast, occurred in the generated cell images. In this paper, a simple but efficient enhancement framework is proposed to address the problems. The framework removes image noises using a hybrid method based on wavelet transform and fuzzy-entropy, and enhances the image contrast with an adaptive morphological approach. Experiments on real cell dataset were made to assess the performance of proposed framework. The experimental results demonstrate that our proposed enhancement framework increases the cell tracking accuracy to an average of 74.49%, which outperforms the benchmark algorithm, i.e., 46.18%.
The paper proposed a segmentation method combining both local and global threshold techniques to efficiently segment
the cell images. Firstly, the image would be divided into several parts, and the Otsu operation would be used to each of
them to detect details. Secondly, main body of the objects would be filtered out by a global threshold algorithm. Finally,
based on the previous steps, more advanced segmentation outcomes can be achieved. The experimental results show that
this algorithm made better performance at detail recognition, such as the cell antennas, which should be very helpful and
important in the medical area.