GT10 Medical Imaging II

Alexandros Tzallas's picture
Alexandros Tzallas
Technological Educational Institute of Epirus (GR)
Ilias Maglogiannis's picture
Ilias Maglogiannis
University of Piraeus (GR)
Conference room
Session time
Saturday, June 24, 2017 - 14:45 to 15:45
A comparative study of cell nuclei attributed relational graphs for knowledge description and categorization in histopathological gastric cancer whole slide images
Abstract: 
In this paper, cell nuclei attributed relational graphs are extensively studied and comparatively analyzed for effective knowledge description and classification in H&E stained whole slide images of gastric cancer. This includes design and implementation of multiple graph variations with diverse tissue component characteristics and architectural properties to obtain enhanced image representations, followed by hierarchical ensemble learning and classification.
Harshita Sharma's picture
Harshita Sharma
Technical University Berlin (DE)
Norman Zerbe's picture
Norman Zerbe
Christine BOger's picture
Christine BOger
Stephan Wienert's picture
Stephan Wienert
Olaf Hellwich's picture
Olaf Hellwich
Peter Hufnagl's picture
Peter Hufnagl
Non-Invasive Assessment of Coronary Stenoses and Comparison to Invasive Techniques: a proof-of-concept study
Abstract: 
Coronary Computed Tomography Angiography (CCTA) has gained substantial ground in everyday clinical practice due to its non-invasive nature. In this work we present a noninvasive method to assess the hemodynamic significance of coronary stenoses using only CCTA images. Two female patients were subjected to Invasive Coronary Angiography, Virtual Histology IVUS and CCTA. The same arterial segment was reconstructed in 3D using the proposed method as well as two already validated 3D reconstruction methods using the aforementioned invasive techniques.
Panagiota Tsompou's picture
Panagiota Tsompou
Panagiotis Siogkas's picture
Panagiotis Siogkas
University of Ioannina (GR)
Antonis Sakellarios's picture
Antonis Sakellarios
Pedro Lemos's picture
Pedro Lemos
Lampros Michalis's picture
Lampros Michalis
Dimitris Fotiadis's picture
Dimitris Fotiadis
University of Ioannina (GR)
Automated collagen proportional area extraction in liver biopsy images using a novel classification via clustering algorithm
Abstract: 
Diagnosis and staging of liver diseases are essential for the therapeutic efficacy of medication and treatment strategies. Measuring the Collagen Proportional Area (CPA) in liver biopsies recently becomes an effective tool for the assessment of fibrosis in liver tissues. State of the art image processing techniques are employed to analyze biopsy images, providing objective assessment of diseases severity. In current work a novel modification of K-means clustering is proposed for image segmentation of liver biopsies. More specifically, supervised restriction of centroids movement is utilized.
Dimosthenis C. Tsouros's picture
Dimosthenis C. Tsouros
Panagiotis N. Smyrlis's picture
Panagiotis N. Smyrlis
Nikolaos Giannakeas's picture
Nikolaos Giannakeas
Alexandros Tzallas's picture
Alexandros Tzallas
Technological Educational Institute of Epirus (GR)
Pinelopi Manousou's picture
Pinelopi Manousou
Dimitrios G. Tsalikakis's picture
Dimitrios G. Tsalikakis
Markos G. Tsipouras's picture
Markos G. Tsipouras
University of Western Macedonia
A Deconstructed Replication of Time of Test Using the AGIS Metric (Skyline paper)
Abstract: 
In medical practice, glaucoma severity is usually measured using the Advanced Glaucoma Intervention Studies (AGIS) metric. In a previous study, we replicated the work of Montolio et al.,and demonstrated that, for a larger dataset, time of day of test using the AGIS metric did make a difference to the measurement of glaucoma, supporting Montolio et al’s work. However, in our earlier study, we used the AGIS scores for both eyes combined. In this paper, we use the measurement from just one eye at a time.
Steve Counsell's picture
Steve Counsell
Stephen Swift's picture
Stephen Swift
Brunel University (UK)
Allan Tucker's picture
Allan Tucker
Brunel University London (UK)