A review for cervical histopathology image analysis using machine vision approaches

Because cervical histopathology image analysis plays a very importation role in the cancer diagnosis and medical treatment processes, since the 1980s, more and more effective machine vision techniques are introduced and applied in this field to assist histopathologists to obtain a more rapid, stable, objective, and quantified analysis result. To discover the inner relation between the visible images and the actual diseases, a variety of machine vision techniques are used to help the histopathologists to get to know more properties and characteristics of cervical tissues, referring to artificial intelligence, pattern recognition, and machine learning algorithms. Furthermore, because the machine vision approaches are usually semi- or full-automatic computer based methods, they are very efficient and labour cost saving, supporting a technical feasibility for cervical histopathology study in our current big data age. Hence, in this article, we comprehensively review the development history of this research field with two crossed pipelines, referring to all related works since 1988 till 2020. In the first pipeline, all related works are grouped by their corresponding application goals, including image segmentation, feature extraction, and classification. By this pipeline, it is easy for histopathologists to have an insight into each special application domain and find their interested applied machine vision techniques. In the second pipeline, the related works on each application goals are reviewed by their detailed technique categories. Using this pipeline, machine vision scientists can see the dynamic of technological development clearly and keep up with the future development trend in this interdisciplinary field.