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Authors: Sarah Ghandour ; Eric Gonneau and Guy Flouzat

Affiliation: LERISM, Toulouse University, France

ISBN: 978-989-8111-69-2

Keyword(s): Segmentation, Watershed Algorithm, Region Adjacency Graph, Mathematical Morphology, Generalized Likelihood Ratio, Clustering, Hypercube Classification.

Related Ontology Subjects/Areas/Topics: Computer Vision, Visualization and Computer Graphics ; Feature Extraction ; Features Extraction ; Image and Video Analysis ; Image Formation and Preprocessing ; Informatics in Control, Automation and Robotics ; Mathematical Morphology ; Segmentation and Grouping ; Signal Processing, Sensors, Systems Modeling and Control ; Statistical Approach

Abstract: In this paper, a new color segmentation scheme of microscopic color images is proposed. The approach combines a region growing method and a clustering method. Each channel plane of the color images is represented by a set of regions using a watershed algorithm. Those regions are represented and modeled by a Region Adjacency Graph (RAG). A novel method is introduced to simplify the RAG by merging candidate regions until the violation of a stopping aggregation criterion determined using a statistical method which combines the generalized likelihood ratio (GLR) and the Bayesian information criterion (BIC). From the resulting segmented and simplified images, the RGB image is computed. Structural features as cells area, shape indicator and cells color are extracted using the simplified graph and then stored in a database in order to elaborate meaningful queries. A regularization step based on the use of an automatic classification will take place. Results show that our method that does not involve any a priori knowledge is suitable for several types of cytology images. (More)

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Paper citation in several formats:
Ghandour S.; Gonneau E.; Flouzat G. and (2009). SEGMENTATION OF MULTISPECTRAL IMAGES USING MATHEMATICAL MORPHOLOGY AND AUTOMATIC CLASSIFICATION - Application to Microscopic Medical Images.In Proceedings of the Fourth International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2009) ISBN 978-989-8111-69-2, pages 237-240. DOI: 10.5220/0001753702370240

@conference{visapp09,
author={Sarah Ghandour and Eric Gonneau and Guy Flouzat},
title={SEGMENTATION OF MULTISPECTRAL IMAGES USING MATHEMATICAL MORPHOLOGY AND AUTOMATIC CLASSIFICATION - Application to Microscopic Medical Images},
booktitle={Proceedings of the Fourth International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2009)},
year={2009},
pages={237-240},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001753702370240},
isbn={978-989-8111-69-2},
}

TY - CONF

JO - Proceedings of the Fourth International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2009)
TI - SEGMENTATION OF MULTISPECTRAL IMAGES USING MATHEMATICAL MORPHOLOGY AND AUTOMATIC CLASSIFICATION - Application to Microscopic Medical Images
SN - 978-989-8111-69-2
AU - Ghandour, S.
AU - Gonneau, E.
AU - Flouzat, G.
PY - 2009
SP - 237
EP - 240
DO - 10.5220/0001753702370240

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