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Image analysis of cells and tissues is an important area of research, since it forms a basis for large number of biomedical applications. Many complexities such as heterogeneous shape of cells/nuclei, overlapping cells, presence of background noise, and variability in staining and illumination conditions make automated analysis of microscopic images, a complicated problem. Cell detection is the most basic and essential step for analysis of microscopic cell images. Even though many methods have been proposed over the years, ongoing research has failed to develop a system which is both generic and trainable for a wider range of applications with sensitivity and accuracy. Hence, novel automated methods are necessary. This paper examines and analyses various automated methods for cell detection and segmentation.