  Cytology is the branch of pathology which deals with the microscopic
examination of cells for diagnosis of carcinoma or inflammatory conditions.
Automation in cytology started in the early 1950s with the aim to reduce manual
efforts in diagnosis of cancer. The inflush of intelligent technological units
with high computational power and improved specimen collection techniques
helped to achieve its technological heights. In the present survey, we focus on
such image processing techniques which put steps forward towards the automation
of cytology. We take a short tour to 17 types of cytology and explore various
segmentation and/or classification techniques which evolved during last three
decades boosting the concept of automation in cytology. It is observed, that
most of the works are aligned towards three types of cytology: Cervical, Breast
and Lung, which are discussed elaborately in this paper. The user-end systems
developed during that period are summarized to comprehend the overall growth in
the respective domains. To be precise, we discuss the diversity of the
state-of-the-art methodologies, their challenges to provide prolific and
competent future research directions inbringing the cytology-based commercial
systems into the mainstream.
