In industrial and biomedical computer vision applications it is very important to deal with digital images which contain a large amount of overlapping objects. This leads to very useful applications such as cell imaging in the healthcare for diagnosis of cancer and nanoparticle imaging in the industry for quality control and material research. Our goal is to study automatic segmentation of overlapping objects. Manual segmentation is laborious, slow, and sensitive to interpretations. Images may contain tens of thousands of objects and it is time-consuming to detect attached and overlapping objects accurately. Recent developments in machine learning, especially with deep neural networks have enabled massive computations with large datasets.

This project continues the work that was done in the COMPHI project where the state-of-the-art methods for nanoparticle segmentation were developed for industrial purposes. The main aim of this project is to extend our approach to cell imaging for the modern digital healthcare.

The project includes international collaboration with the Laboratory for Image and Video Engineering (LIVE) at The University of Texas at Austin (UT) led by Professor Alan C. Bovik.

LUT        Academy of Finland


Sahar Zafari E-mail visiting UT Doctoral student
Tuomas Eerola E-mail Office: 2422 Tel: +358 40 139 3405 Post-Doctoral Researcher, Project Manager
Jouni Sampo E-mail Office: 2508 Tel: +358 44 337 5672 University Lecturer
Heikki Kälviäinen E-mail Office: 2415 Tel: +358 40 586 7552 Professor, Project Leader