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Authors: Harshita Sharma 1 ; Norman Zerbe 2 ; Daniel Heim 2 ; Stephan Wienert 3 ; Hans-Michael Behrens 4 ; Olaf Hellwich 1 and Peter Hufnagl 2

Affiliations: 1 Technische Universität Berlin, Germany ; 2 Charité - Universitätsmedizin Berlin, Germany ; 3 VMscope GmbH, Germany ; 4 Charité - Universitätsmedizin Berlin and Christian-Albrechts University, Germany

ISBN: 978-989-758-091-8

Keyword(s): Histopathology, H&E, Gastric Cancer, Nuclei segmentation, Virtual microscopy, Image analysis, Multiresolution, AdaBoost.

Related Ontology Subjects/Areas/Topics: Applications and Services ; Computer Vision, Visualization and Computer Graphics ; Early and Biologically-Inspired Vision ; Features Extraction ; Image and Video Analysis ; Medical Image Applications ; Segmentation and Grouping

Abstract: This paper describes a multi-resolution technique to combine diagnostically important visual information at different magnifications in H&E whole slide images (WSI) of gastric cancer. The primary goal is to improve the results of nuclei segmentation method for heterogeneous histopathological datasets with variations in stain intensity and malignancy levels. A minimum-model nuclei segmentation method is first applied to tissue images at multiple resolutions, and a comparative evaluation is performed. A comprehensive set of 31 nuclei features based on color, texture and morphology are derived from the nuclei segments. AdaBoost classification method is used to classify these segments into a set of pre-defined classes. Two classification approaches are evaluated for this purpose. A relevance score is assigned to each class and a combined segmentation result is obtained consisting of objects with higher visual significance from individual magnifications, thereby preserving both coarse and fine details in the image. Quantitative and visual assessment of combination results shows that they contain comprehensive and diagnostically more relevant information than in constituent magnifications. (More)

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Paper citation in several formats:
Sharma, H.; Zerbe, N.; Heim, D.; Wienert, S.; Behrens, H.; Hellwich, O. and Hufnagl, P. (2015). A Multi-resolution Approach for Combining Visual Information using Nuclei Segmentation and Classification in Histopathological Images.In Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2015) ISBN 978-989-758-091-8, pages 37-46. DOI: 10.5220/0005247900370046

@conference{visapp15,
author={Harshita Sharma. and Norman Zerbe. and Daniel Heim. and Stephan Wienert. and Hans{-}Michael Behrens. and Olaf Hellwich. and Peter Hufnagl.},
title={A Multi-resolution Approach for Combining Visual Information using Nuclei Segmentation and Classification in Histopathological Images},
booktitle={Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2015)},
year={2015},
pages={37-46},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005247900370046},
isbn={978-989-758-091-8},
}

TY - CONF

JO - Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2015)
TI - A Multi-resolution Approach for Combining Visual Information using Nuclei Segmentation and Classification in Histopathological Images
SN - 978-989-758-091-8
AU - Sharma, H.
AU - Zerbe, N.
AU - Heim, D.
AU - Wienert, S.
AU - Behrens, H.
AU - Hellwich, O.
AU - Hufnagl, P.
PY - 2015
SP - 37
EP - 46
DO - 10.5220/0005247900370046

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