Background
Complex 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 of modern imaging datasets exceeds human analysis ability. In this project we will overcome these limitations through the development of novel intensity-independent image informatics approaches exploring low-contrast features to provide a key methodology for the quantitative understanding of the role of complex biological networks in life systems.
Aims:
develop intensity-independent image analysis and processing solutions to extract and characterise 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 10^6 of links across a range of physical scales;
build a unique benchmarking repository of complex biological networks with their topological characteristics.
The approaches developed here will enable robust extraction and quantitative characterisation of the architecture of 2D/3D/3D time-series biological networks. These quantitative measures will allow researchers to understand in which way topology and functions of the biological networks are related. This will then open new avenues, especially for researchers exploring the importance of fungal networks in causing diseases in crops, and of leaf veins and cytoskeletal networks in plant growth. Most importantly, adaptation of the proposed approaches need not be limited to biological images but can be applied to any images that contain curvilinear features. Specifically, the approach for a low-contrast feature extraction will be extremely beneficial to both the academic and industrial computing and bioimaging communities, as it will allow the confident use of low-contrast features in a wide range of different domains, such as biomedical imaging, robotics, astronomy, security and art, where image processing methods also play an essential role.
The applicant should have:
BSc/MSc in Computer Science, Engineering, Physics or Mathematics
BSc/MSc thesis within Image Processing, Computer Vision, Visualisation, Bioinformatics, …
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
UK citizenship or EU citizenship
For further information please contact Dr Boguslaw Obara (boguslaw [dot] obara [AT] durham [dot] ac [dot] uk)
