This paper summarizes the recent advances in image processing methods for morphological cell analysis. The topic of morphological analysis has received much attention with the increasing demands in both bioinformatics and biomedical applications. Among many factors that affect the diagnosis of a disease, morphological cell analysis and statistics have made great contributions to results and effects for a doctor. Morphological cell analysis finds the cellar shape, cellar regularity, classification, statistics, diagnosis, and so forth. In the last 20 years, about 1000 publications have reported the use of morphological cell analysis in biomedical research. Relevant solutions encompass a rather wide application area, such as cell clumps segmentation, morphological characteristics extraction, 3D reconstruction, abnormal cells identification, and statistical analysis. These reports are summarized in this paper to enable easy referral to suitable methods for practical solutions. Representative contributions and future research trends are also addressed.

Existing image color difference metrics are not appropriate to predict the preserved image color difference when comparing images under mixed illumination. We propose a novel image color difference metric that apply the chromatic adaptation into image color difference model to obtain an objective image color difference. Meanwhile, a proper workflow for evaluating image color difference based on this metric is proposed. Subjective and objective experiments are properly designed and processed. The results indicate that the proposed image color difference metric is able to predict the preserved image color difference well.

In this paper, the preprocessing method for improving the quality of interferograms is described. The method consists of three processing steps---azimuth filtering, range filtering and oversampling. It is shown that the first two steps are effective to eliminate the decorrelation caused by spectrum misalignment in azimuth and range and the oversampling can eliminate the error introduced by the aliasing because of convolution in frequency domain. The validity of the proposed method is verified by ERS SLC data.