Name: Boguslaw Obara

Profile: AI & Imaging Data Scientist, Innovator, & Consultant. Founder & Director of Gliff.AI (football, chocolate & travel nut)

Email: boguslaw [dot] obara [AT] newcastle [dot] ac [dot] uk

Research and Professional Skills

Image Processing 95%
Pattern Recognition 85%
Machine Learning 60%
Problem Solving 100%
Leadership 80%
About me

Boguslaw Obara (BO) is a Professor of Image Informatics in the School of Computing at Newcastle University. He obtained a first master degree in computational physics from Jagiellonian University and PhD in Image Processing from AGH University of Science and Technology.

Before joining Newcastle University as Professor; he held research assistant positions at Polish Academy of Sciences and at Computer Vision Laboratory, ETH, Fulbright Fellowship at Vision Research Laboratory, postdoctoral positions at Center for BioImage Informatics, University of California, and at Oxford e-Research Centre, University of Oxford, and assistant, associate and professor positions at Department of Computer Science at Durham University. BO was also AstraZeneca Visiting Professor in Image Processing & Artificial Intelligence.

BO's research focuses on the design and implementation of complex image analysis and processing, pattern recognition, computer vision and machine learning solutions applied to a wide range of domains.



PhD Students:

Alumni Visitors:
  • 2020
    • Zeinab Almahdi Mohammed Haroon (African Institute for Mathematical Sciences)
    • Mayank Patel (Indian Institute of Technology)
    • Prof. Caiyun Wang (Nanjing University of Aeronautics and Astronautics)
  • 2019
    • Romaissa Mekhzouni (ENSTA ParisTech)
    • Yagiz Batu Saatci (Antalya Bilim University)
    • Agnieszka Stankiewicz (Poznan University of Technology)
    • Cesar Alberto Cigarroa Constantino (Universidad Politecnica de Chiapas)
  • 2017
    • Prof. Mark Fricker (Oxford University)
  • 2015
    • Dr Bartosz Ziółko (AGH University of Science and Technology)
  • 2014
    • Prof. Stéphanie Portet (University of Manitoba)
    • Agnieszka Karpińska (Cracow University of Technology)
    • Dr Bartosz Schramm (Jagiellonian University)

Alumni Staff:

Image Formation and Digitization

Image acquisition is the first step of digital image processing and is often not properly taken into account. Quantitative analysis of any images requires a good understanding of the image formation and digitization process.

Image Processing and Pattern Recognition

Pattern recognition is used to automatically extract meaningful features from digital images. Over the past years, our team has proposed a wide range of novel image informatics approaches for blob, network, and surface patterns recognition in multi-type, -scale, -dimensional imaging datasets.

Best Practices for Image-Driven Solutions

Over the past years, our team has focused on standardisation, integrity, and quality of image-driven information and processes, from imaging data acquisition, storing, sharing, annotation, processing, to analytics.

Research Projects

Image Processing

Macular Oedema

The objectives of this project are to develop and assess novel image analysis techniques of 3D OCT scans for volumetric assessment of different morphological patterns in diabetic macular oedema (DMO) such as: 1) Overall volume of the generalised outer retinal thickening in early DMO. 2) Volume of accumulated serous fluid under the neurosensory retina (SRF). 3) Residual volume of retinal tissue passing between the inner and outer plexiform retinal layers, which is an optimal measurement of potential residual macular function.

Image Processing

Fungal Networks

Saprotrophic fungi are critical in ecosystem biology as they are the only organisms capable of complete degradation of wood in temperate forests. The architecture of the network continuously adapts to local nutritional cues, damage or predation, through growth, branching, fusion or regression. We propose an image processing approach for fungal network segmentation and graph-based analysis.

Image Processing

Cytoskeletal Networks

A 3D/4D cytoskeletal network may contain thousands of nodes and connections, in specific but variable geometric organisations which vary with time. Such data exceeds the capacity of human analysis, creating a barrier, which can only be overcome with robust, automated image analysis and informatics tools to extract, characterise and model networks.

Image Processing


Graph embedding techniques aim to automatically create a low-dimensional representation of a given graph, which captures key structural elements in the resulting embedding space. We demonstrate that several topological features are indeed being approximated by the embedding space, allowing key insight into how graph embeddings create good representations.

Best Practices

GPU-Based Segmentation

The local Gaussian distribution fitting energy model is a state-of-the-art method, capable of segmenting inhomogeneous objects with poorly defined boundaries, but it is computationally expensive. In our approach, we port the energy functional to the GPU, and introduce a novel set of interactive brush functions to segment challenging datasets where an active contour would not ordinarily capture the target object.

Image Processing


This project focuses on a robust method for extracting a natural interpolating 3D piecewise cubic spline from a 2D input image of a helix object. We are able to use the generated spline to extract 3D metrics such as tortuosity and curvature. The algorithm analytically chooses locations to sample the image to extract properties of the curve, such as its amplitude and perpendicular width, to ensure robustness. The generated 3D spline has few input parameters, and only requires a single view of the 2D dataset, making it suitable for a range of applications in engineering, physics, and biology.

Best Practices

Big Data

Projects focuses on big data challenges including capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating and information privacy.

Image Processing


The colocalization technique is important to many cell biological and physiological studies during the demonstration of a relationship between pairs of bio-molecules.

Image Processing

Deep Learning

Research, development and application of a wide range of machine learning models to perform classification tasks directly from images, text, or sound. Models are trained by using a large set of labeled data and neural network architectures that contain many layers.

Image Processing


We introduce a new method for the detection of parametrisable shapes in N dimensions; this method has been developed for ellipses and uses simple morphological operations, building on aspects of granulometry and signal processing techniques. With this method it is possible to easily extract the position, size and rotation of elliptical objects in any image data. This method is low parameter, accurate and robust to noise and object clustering in greyscale images; key contributions are the abiltiy to find an unknown number of ellipses with no a priori information and no arbitrary thresholding and robustness to both noise and clustering.

Image Processing


In this project we demonstrate the evolution of a disordered composite material, using voltages as the external stimuli, into a form where a simple computational problem can be solved. The material consists of single-walled carbon nanotubes suspended in liquid crystal; the nanotubes act as a conductive network, with the liquid crystal providing a host medium to allow the conductive network to reorganise when voltages are applied. We show that the application of electric fields under computer control results in a significant change in the material morphology, favouring the solution to a classification task.

Image Processing


Differential interference contrast microscopy plays an important role in modern bacterial cell biology. We have developed and evaluated a high-throughput image analysis and processing approach to detect and characterize bacterial cells and chemotaxis proteins. Its performance was evaluated using differential interference contrast and fluorescence microscopy images of Rhodobacter sphaeroides.

Image Processing

Cell Tip Tracking

Fungi cause devastating plant and human diseases. There is considerable evidence that much of the cellular machinery driving growth of invasive fungal hyphae is common across all fungi, including plant and mammalian pathogens, and involves localized tip growth. We have developed and evaluated high-throughput automated microscope-based multi-dimensional image analysis systems to segment and characterize fungal growth, and characterize the patterns of protein localization within the tip that control development.

Image Processing

Ascidian Cells Tracking

A method to automatically segment notochord cell boundaries from differential interference contrast (DIC) timelapse images of the elongating ascidian tail. The method is based on a specialized parametric active contour, the network snake, which can be initialized as a network of arbitrary but fixed topology and provides an effective framework for simultaneously segmenting multiple touching cells.

Image Processing

Cancer Cell Protrusions

Identification, description and quantification of the cell behavior observed by time-lapse confocal microscopy Research, development and implementation of novel 4D image analysis and processing methods for identification, description and quantification of the cell behavior (protrusion, adhesion and invasion), as observed by time-lapse confocal microscopy.

Image Processing

Change Detection

Runways are vital descriptive features of airports and knowledge of their location is important to many aviation and military applications. With the recent wide availability of remote sensing data, there is demand for an automatic process of extracting runway geometry from satellite imagery. In this project we establish a novel method for accurate and precise extraction of geometric polygons for an arbitrary number of runways in VHR remote sensing imagery.

Best Practices


Bisque provides a platform for data sharing, flexible metadata management, and integrated extensible image analysis and processing. Bisque system is used by many research and commercial institutions including: 1) iPlant: a community of researchers, educators, and students working to enrich all plant sciences through the development of cyberinfrastructure that are essential components of modern biology. 2) Pfizer – The Pfizer Neuroscience Research Unit is developing quantitative methods that process the images, keeping track of the processing workflow, and allowing scientists to create new work flows that seamlessly combine multimodal data.