PhD in Computer Science at Durham University, UKBioImage Informatics for Spatio-Temporal Biological Networks (BIONET)
SupervisorDr Boguslaw ObaraSchool of Engineering and Computing Sciences, Durham UniversitySouth Road, DH1 3LE, Durham, UKPhone: +44 19133 42431Email: firstname.lastname@example.org
Duration of Award3.5 Years
BackgroundComplex networks are fundamental to transport and communication in biological systems, but
little is known about their architecture and dynamics due to the fact that the size and complexity ofmodern imaging datasets exceeds human analysis ability. In this project we will overcome theselimitations through the development of novel intensity-independent image informatics approaches ex-ploring low-contrast features to provide a key methodology for the quantitative understanding of therole of complex biological networks in life systems.
The project aims to: develop intensity-independent image analysis and processing solutions to extract and charac-
terise the architecture of structural biological networks from 2D/3D/3D time-series images; validate the proposed approaches using images of fungal, leaf vein and cytoskeletal networks
with 106 of links across a range of physical scales; build a unique benchmarking repository of complex biological networks with their topological
3D microscopic image of networks ofinterconnected actin filaments. Scalebar corresponds to 100m.
The approaches developed here will enable robust extrac-tion and quantitative characterisation of the architecture of2D/3D/3D time-series biological networks. These quantitativemeasures will allow researchers to understand in which waytopology and functions of the biological networks are related.This will then open new avenues, especially for researchersexploring the importance of fungal networks in causing dis-eases in crops, and of leaf veins and cytoskeletal networks inplant growth. Most importantly, adaptation of the proposed ap-proaches need not be limited to biological images but can beapplied to any images that contain curvilinear features. Specif-ically, the approach for a low-contrast feature extraction will beextremely beneficial to both the academic and industrial computing and bioimaging communities, asit will allow the confident use of low-contrast features in a wide range of different domains, such asbiomedical imaging, robotics, astronomy, security and art, where image processing methods alsoplay an essential email@example.com
Entry RequirementsThe applicant should have:
MSc in Computer Science, Engineering, Physics or Mathematics. MSc thesis within Image Processing, Computer Vision, and Visualisation. Excellent programming skills, experience in MATLAB and Java/C++. A solid background in mathematics and statistics. Knowledge on depth image data analysis and processing. Excellent communication skills in English, both spoken and written.
How to ApplyApplicants can make initial informal enquiries with Dr Boguslaw Obara (http://community.dur.ac.uk/boguslaw.obara/). If you meet the eligibility criteria please make an application via the universityapplications page at: https://www.dur.ac.uk/postgraduate/apply/. On your application pleasespecify - Project title: BioImage Informatics; Supervisor: Dr Boguslaw Obara.http://community.dur.ac.uk/boguslaw.obara/join-us/research-fellowships/http://community.dur.ac.uk/boguslaw.obara/http://community.dur.ac.uk/boguslaw.obara/https://www.dur.ac.uk/postgraduate/apply/