Development of Image Processing and Machine Learning based Tools for Analysis of Phase-Contrast Optical Microscopy Time Series Images. TÜBİTAK ARDEB 1001. 2020-2023

    In this project image processing and machine learning based approaches will be developed for morphological and movement analyses of breast cancer cell lines from phase-contrast optical microscopy time series images. Towards this aim our focus will be on the biological problems of cell motility (the case where a low number of isolated cells independently translocate) and wound healing (the case where cells move as tightly or loosely associated cohesive group).

A new Network of European BioImage Analysts to advance life science imaging (NEUBIAS). EU Cost Action CA15124. 2016-2020

    This Action is a programme for establishing a network of BioImage Analysts (BIAlysts), in order to maximize the impact of advances in imaging technology on the Life-Sciences (LSc), and to boost the productivity of bioimaging-based research projects in Europe.

Probabilistic and Machine Learning-based Methods for Automatic Dendritic Spine Segmentation, Classification, and Tracking in Two-Photon Microscopy Images. TÜBİTAK ARDEB 1001. 2014-2017

    This project aims for developing new probabilistic and machine-learning based image processing algorithms for the analysis of learning-related changes in dendritic spines from time-series two-photon microscopy data.

(CORDEN) Comparing Visual and computer-based ratings of dementia-related neuroimaging findings. TÜBİTAK ARDEB 3501 Career Grant. 2011-2014

    This project is a collaborative effort between Bayındır Hospital İçerenköy and Bahçeşehir University that aims in

  1. Computer-based measurement of dementia-related neuro-imaging findings from MR images acquired at routine clinical practice.
  2. Investigation of the relationship between computer-based measurements and visual assessment by neurology radiology experts.

İstanbul Preserves her Cultural Heritage, Museums Revive in 3D. İstanbul Development Agency. 2012-2013

    The aim of this project is to exploit information technologies for preservation and effective presentation of cultural and historical heritage in Istanbul.

(ACaVaS) Automated Cardiac Valves Segmentation. TÜBİTAK ARDEB 1002. 2011-2012

    The project aims at 3D fully-automatic segmentation and volumetric reconstruction of the aortic and mitral valves from contrast enhanced CT images.

(Pre-Surgical) 4D Modeling of the Human Heart for Pre-Surgical Planning. San-Tez, Ministry of Industry and Trade. 2011-2012

    The present proposal aims at developing a method to obtain a 4-dimensional (space and time) cardiac model, which is both patient-specific and interactive without requiring any information other than that provided by conventional CT technology.

(COSeRMI) Content and Ontology based Search and Retrieval of Medical Images. EU FP7 Marie Curie ERG. 2010-2012

    This reintegration project aims at combining content and context information for search and retrieval of brain magnetic resonance (MR) images from large repositories.

(IRonDB) MR-Based Analysis, Indexing, and Retrieval of Brain Iron Deposition in Basal Ganglia. EU FP6 Marie Curie ToK. 2007-2010

This multi-disciplinary project targets to significantly improve the understanding of neurodegenerative diseases by developing automatic methods that enable relating the brain iron accumulation to various diseases and complications.
The project involves a multi-disciplinary research requiring novel solutions from medical image processing, pattern recognition, search and retrieval, and clinical science.
This is a joint project with Philips Research Eindhoven.