PhD Studentship: Intelligent algorithms for histological imaging

United Kingdom
Jan 12, 2018
Apr 09, 2018
Organization Type
University and College
Full Time

This project is focused on the development of new computational approaches to resolve some fundamental problems related to the extraction of quantifiable information from microscopy images. The aim is to investigate and develop “intelligent” procedures and work-flows best suited for the analysis of microscopy images of cells and tissues (e.g. for the diagnosis of cancer). To this purpose, the researcher will investigate how imaging programs and procedures can be used to identify and quantify cellular and tissue morphology as observed in digitised images of tissue samples, as well as characterising staining patterns for the analysis of the expression of key molecular makers in those tissues. By “intelligent algorithms” we mean programs that exploit context-based information and spatial reasoning so it can be used to mechanically reason about image contents, so it can facilitate further analysis on areas of interest that depart from expected ranges of normality and facilitate reaching diagnostic and prognostic conclusions about the tissues analysed.


Funding Notes

At present, we would only consider applications from self-funded prospective students with:

  • a good biomedical or engineering/computing degree (minimum of a 2:1), with interests in the areas outlined above,
  • good command of the English language (written and spoken) as outlined in the postgraduate prospectus,
  • competent with computers and data handling (ideally with Java or C language programming skills),
  • a source of funding to cover tuition fees and bench fees (note that tuition fees are different for Home and EU students than for International students).


For information regarding the project, please contact Prof G. Landini (



  • Randell DA, Landini G, Galton A. Discrete mereotopology for spatial reasoning in automated histological image analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 35 (3):568-581, 2013.
  • Landini G. Fractals in Microscopy. Journal of Microscopy 241, Pt1: 1-8, 2011.
  • Landini G, Randell DA, Breckon T, Han JW. Morphologic characterization of cell neighborhoods in neoplastic and preneoplastic epithelium. Analytical and Quantitative Cytology & Histology 32:30-38, 2010.
  • Landini G, Othman IE. Architectural analysis of oral cancer, dysplastic and normal epithelia. Cytometry A 61A:45-55, 2004.


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