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Authors: Santa Di Cataldo ; Elisa Ficarra and Enrico Macii

Affiliation: Politecnico di Torino, Italy

ISBN: 978-989-8111-18-0

Keyword(s): Tissue segmentation, tissue confocal images, immunohistochemistry, K-means clustering, Support Vector Machine.

Related Ontology Subjects/Areas/Topics: Applications and Services ; Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computer Vision, Visualization and Computer Graphics ; Data Manipulation ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Medical Image Detection, Acquisition, Analysis and Processing ; Methodologies and Methods ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Soft Computing

Abstract: In this paper we present a fully-automated method for the detection of tumor areas in immunohistochemical confocal images. The image segmentation provided by the proposed technique allows quantitative protein activity evaluation on the target tumoral tissue disregarding tissue areas that are not affected by the pathology, such as connective tissue. The automated method, that is based on an innovative unsupervised clustering approach, enables more accurate tissue segmentation compared to traditional supervised methods that can be found in literature, such as Support Vector Machine (SVM). Experimental results conducted on a large set of heterogeneous immunohistochemical lung cancer tissue images demonstrate that the proposed approach overcomes the performance of SVM by 8%, achieving on average an accuracy of 90%.

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Paper citation in several formats:
Di Cataldo S.; Ficarra E.; Macii E. and (2008). FULLY-AUTOMATED SEGMENTATION OF TUMOR AREAS IN TISSUE CONFOCAL IMAGES - Comparison between a Custom Unsupervised and a Supervised SVM Approach.In Proceedings of the First International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2008) ISBN 978-989-8111-18-0, pages 116-123. DOI: 10.5220/0001068501160123

@conference{biosignals08,
author={Santa {Di Cataldo} and Elisa Ficarra and Enrico Macii},
title={FULLY-AUTOMATED SEGMENTATION OF TUMOR AREAS IN TISSUE CONFOCAL IMAGES - Comparison between a Custom Unsupervised and a Supervised SVM Approach},
booktitle={Proceedings of the First International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2008)},
year={2008},
pages={116-123},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001068501160123},
isbn={978-989-8111-18-0},
}

TY - CONF

JO - Proceedings of the First International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2008)
TI - FULLY-AUTOMATED SEGMENTATION OF TUMOR AREAS IN TISSUE CONFOCAL IMAGES - Comparison between a Custom Unsupervised and a Supervised SVM Approach
SN - 978-989-8111-18-0
AU - Di Cataldo, S.
AU - Ficarra, E.
AU - Macii, E.
PY - 2008
SP - 116
EP - 123
DO - 10.5220/0001068501160123

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