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Medical Imaging

Future Processing is involved in the challenging and ground-breaking area of medical imaging. We work in close co-operation with medical imaging experts, research institutions and clinics worldwide.

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We have worked on a wide variety of software development projects including the extraction of textural information from images of various modalities, real-time image collaboration tools, statistical image analysis and fluoroscopy acquisition systems.

Our latest projects are focused on dynamic contrast enhanced imaging and on the analysis of computed tomography images.

The project

Enhancing the diagnostic efficiency of dynamic Contrast-enhanced imaging in personalised Oncology by extracting New and Improved Biomarkers (ECONIB).

February 2016 – December 2018

Dynamic contrast enhanced (DCE) imaging using computed tomography (CT) or magnetic resonance (MR) has been intensively studied to allow for assessing the vascular support of various tumours and other tissues. DCE biomarkers were shown to be correlated with physiological and molecular processes which can be observed in tumour angiogenesis (morphologically characterised by an increased number of micro-vessels).

The aim is to advance the diagnostic efficiency of dynamic contrast-enhanced imaging in personalised oncology by extracting new and improved biomarkers. The platform will provide novel algorithms in the fields of medical imaging, machine learning and DCE analysis which will help improve the diagnostic efficiency of the DCE imaging.

The novel algorithms will enable physicians to extract and analyse new biomarkers. Advanced statistical tools will be useful to investigate biological correlates of the extracted image biomarkers. Our aim is to implement an innovative system and bring it into a day-to-day clinical practice to significantly improve cancer care.

Meet the team

The teams consist of experienced medical imaging and image processing scientists. Below are the key personnel involved in and supervising medical imaging and image processing projects at Future Processing.

Jakub Nalepa

Jakub Nalepa, is also working as a Researcher at The Institute of Informatics, Silesian University of Technology, Gliwice, Poland. His research interests encompass evolutionary algorithms, especially (adaptive and self-adaptive) genetic and memetic algorithms, pattern recognition, optimization, medical imaging, parallel computing, and interdisciplinary applications of these methods. He has been involved in several projects related to the above-mentioned domains in both academia and industry. So far, he has published more than 30 papers in these fields. He is a Reviewer for several international journals and conferences. He is an IEEE member.

Google scholar: https://scholar.google.pl/citations?user=kt6EnKcAAAAJ&hl=en
Research gate: https://www.researchgate.net/profile/Jakub_Nalepa
DBLP profile: http://dblp.uni-trier.de/pers/hd/n/Nalepa:Jakub

Michał Kawulok

Michał Kawulok, is also working as an Assistant Professor at The Institute of Informatics, Silesian University of Technology, Gliwice, Poland. His general research interests are concerned with image analysis and pattern recognition, with particular attention given to support vector machines, gesture recognition, facial image analysis, medical imaging, and image colorisation. He is an IEEE member.

Google scholar: https://scholar.google.pl/citations?user=6rK_xoMAAAAJ&hl=en
Research gate: https://www.researchgate.net/profile/Michal_Kawulok
DBLP profile: http://dblp.uni-trier.de/pers/hd/k/Kawulok:Michal

Janusz Szymanek

Janusz Szymanek has been instrumental in all medical imaging projects at Future Processing and is the system architect and key developer. He has a broad experience in research projects and has published several papers on image processing and pattern recognition.

Google scholar: https://scholar.google.pl/citations?user=Wwpp3g2JI7YC&hl=en
Research gate: https://www.researchgate.net/researcher/2053771694_Janusz_Szymanek
DBLP profile: http://dblp.uni-trier.de/pers/hd/s/Szymanek:Janusz

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